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Lee, Sukjoon, and Donghee Shim. "Analysis for the KDD Cup 1999 Data Using the Convolutional Neural Network." Journal of Next-generation Convergence Information Services Technology 10, no. 2 (April 30, 2021): 123–32. http://dx.doi.org/10.29056/jncist.2021.04.02.
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Serinelli, Benedetto Marco, Anastasija Collen, and Niels Alexander Nijdam. "Training Guidance with KDD Cup 1999 and NSL-KDD Data Sets of ANIDINR: Anomaly-Based Network Intrusion Detection System." Procedia Computer Science 175 (2020): 560–65. http://dx.doi.org/10.1016/j.procs.2020.07.080.
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Xia, Yong Xiang, Zhi Cai Shi, Yu Zhang, and Jian Dai. "A SVM Intrusion Detection Method Based on GPU." Applied Mechanics and Materials 610 (August 2014): 606–10. http://dx.doi.org/10.4028/www.scientific.net/amm.610.606.
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To optimize training procedure of IDS based on SVM and reduce time consumption, a SVM intrusion detection method based on GPU is proposed in the study. During the simulation experiments with KDD Cup 1999 data, GPU-based parallel computing model is adopted. Results of the simulation experiments demonstrate that time consumption in the training procedure of IDS is reduced, and performance of IDS is kept as usual.
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Seo, Jae-Hyun, and Yong-Hyuk Kim. "Machine-Learning Approach to Optimize SMOTE Ratio in Class Imbalance Dataset for Intrusion Detection." Computational Intelligence and Neuroscience 2018 (November 1, 2018): 1–11. http://dx.doi.org/10.1155/2018/9704672.
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The KDD CUP 1999 intrusion detection dataset was introduced at the third international knowledge discovery and data mining tools competition, and it has been widely used for many studies. The attack types of KDD CUP 1999 dataset are divided into four categories: user to root (U2R), remote to local (R2L), denial of service (DoS), and Probe. We use five classes by adding the normal class. We define the U2R, R2L, and Probe classes, which are each less than 1% of the total dataset, as rare classes. In this study, we attempt to mitigate the class imbalance of the dataset. Using the synthetic minority oversampling technique (SMOTE), we attempted to optimize the SMOTE ratios for the rare classes (U2R, R2L, and Probe). After randomly generating a number of tuples of SMOTE ratios, these tuples were used to create a numerical model for optimizing the SMOTE ratios of the rare classes. The support vector regression was used to create the model. We assigned each instance in the test dataset to the model and chose the best SMOTE ratios. The experiments using machine-learning techniques were conducted using the best ratios. The results using the proposed method were significantly better than those of previous approach and other related work.
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Sharma, Srishti, Yogita Gigras, Rita Chhikara, and Anuradha Dhull. "Analysis of NSL KDD Dataset Using Classification Algorithms for Intrusion Detection System." Recent Patents on Engineering 13, no. 2 (May 27, 2019): 142–47. http://dx.doi.org/10.2174/1872212112666180402122150.
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Background: Intrusion detection systems are responsible for detecting anomalies and network attacks. Building of an effective IDS depends upon the readily available dataset. This dataset is used to train and test intelligent IDS. In this research, NSL KDD dataset (an improvement over original KDD Cup 1999 dataset) is used as KDD’99 contains huge amount of redundant records, which makes it difficult to process the data accurately. Methods: The classification techniques applied on this dataset to analyze the data are decision trees like J48, Random Forest and Random Trees. Results: On comparison of these three classification algorithms, Random Forest was proved to produce the best results and therefore, Random Forest classification method was used to further analyze the data. The results are analyzed and depicted in this paper with the help of feature/attribute selection by applying all the possible combinations. Conclusion: There are total of eight significant attributes selected after applying various attribute selection methods on NSL KDD dataset.
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Cheng, Guo Zhen, Dong Nian Cheng, and He Lei. "A Novel Network Traffic Anomaly Detection Based on Multi-Scale Fusion." Applied Mechanics and Materials 48-49 (February 2011): 102–5. http://dx.doi.org/10.4028/www.scientific.net/amm.48-49.102.
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Detecting network traffic anomaly is very important for network security. But it has high false alarm rate, low detect rate and that can’t perform real-time detection in the backbone very well due to its nonlinearity, nonstationarity and self-similarity. Therefore we propose a novel detection method—EMD-DS, and prove that it can reduce mean error rate of anomaly detection efficiently after EMD. On the KDD CUP 1999 intrusion detection evaluation data set, this detector detects 85.1% attacks at low false alarm rate which is better than some other systems.
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Kim, Jiyeon, Jiwon Kim, Hyunjung Kim, Minsun Shim, and Eunjung Choi. "CNN-Based Network Intrusion Detection against Denial-of-Service Attacks." Electronics 9, no. 6 (June 1, 2020): 916. http://dx.doi.org/10.3390/electronics9060916.
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As cyberattacks become more intelligent, it is challenging to detect advanced attacks in a variety of fields including industry, national defense, and healthcare. Traditional intrusion detection systems are no longer enough to detect these advanced attacks with unexpected patterns. Attackers bypass known signatures and pretend to be normal users. Deep learning is an alternative to solving these issues. Deep Learning (DL)-based intrusion detection does not require a lot of attack signatures or the list of normal behaviors to generate detection rules. DL defines intrusion features by itself through training empirical data. We develop a DL-based intrusion model especially focusing on denial of service (DoS) attacks. For the intrusion dataset, we use KDD CUP 1999 dataset (KDD), the most widely used dataset for the evaluation of intrusion detection systems (IDS). KDD consists of four types of attack categories, such as DoS, user to root (U2R), remote to local (R2L), and probing. Numerous KDD studies have been employing machine learning and classifying the dataset into the four categories or into two categories such as attack and benign. Rather than focusing on the broad categories, we focus on various attacks belonging to same category. Unlike other categories of KDD, the DoS category has enough samples for training each attack. In addition to KDD, we use CSE-CIC-IDS2018 which is the most up-to-date IDS dataset. CSE-CIC-IDS2018 consists of more advanced DoS attacks than that of KDD. In this work, we focus on the DoS category of both datasets and develop a DL model for DoS detection. We develop our model based on a Convolutional Neural Network (CNN) and evaluate its performance through comparison with an Recurrent Neural Network (RNN). Furthermore, we suggest the optimal CNN design for the better performance through numerous experiments.
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Krishnan Sadhasivan, Dhanalakshmi, and Kannapiran Balasubramanian. "A Fusion of Multiagent Functionalities for Effective Intrusion Detection System." Security and Communication Networks 2017 (2017): 1–15. http://dx.doi.org/10.1155/2017/6216078.
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Provision of high security is one of the active research areas in the network applications. The failure in the centralized system based on the attacks provides less protection. Besides, the lack of update of new attacks arrival leads to the minimum accuracy of detection. The major focus of this paper is to improve the detection performance through the adaptive update of attacking information to the database. We propose an Adaptive Rule-Based Multiagent Intrusion Detection System (ARMA-IDS) to detect the anomalies in the real-time datasets such as KDD and SCADA. Besides, the feedback loop provides the necessary update of attacks in the database that leads to the improvement in the detection accuracy. The combination of the rules and responsibilities for multiagents effectively detects the anomaly behavior, misuse of response, or relay reports of gas/water pipeline data in KDD and SCADA, respectively. The comparative analysis of the proposed ARMA-IDS with the various existing path mining methods, namely, random forest, JRip, a combination of AdaBoost/JRip, and common path mining on the SCADA dataset conveys that the effectiveness of the proposed ARMA-IDS in the real-time fault monitoring. Moreover, the proposed ARMA-IDS offers the higher detection rate in the SCADA and KDD cup 1999 datasets.
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Obimbo, Charlie, and Matthew Jones. "Applying Variable Coe_cient functions to Self-Organizing Feature Maps for Network Intrusion Detection on the 1999 KDD Cup Dataset." Procedia Computer Science 8 (2012): 333–37. http://dx.doi.org/10.1016/j.procs.2012.01.069.
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Albahar, Marwan Ali. "Recurrent Neural Network Model Based on a New Regularization Technique for Real-Time Intrusion Detection in SDN Environments." Security and Communication Networks 2019 (November 18, 2019): 1–9. http://dx.doi.org/10.1155/2019/8939041.
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Software-defined networking (SDN) is a promising approach to networking that provides an abstraction layer for the physical network. This technology has the potential to decrease the networking costs and complexity within huge data centers. Although SDN offers flexibility, it has design flaws with regard to network security. To support the ongoing use of SDN, these flaws must be fixed using an integrated approach to improve overall network security. Therefore, in this paper, we propose a recurrent neural network (RNN) model based on a new regularization technique (RNN-SDR). This technique supports intrusion detection within SDNs. The purpose of regularization is to generalize the machine learning model enough for it to be performed optimally. Experiments on the KDD Cup 1999, NSL-KDD, and UNSW-NB15 datasets achieved accuracies of 99.5%, 97.39%, and 99.9%, respectively. The proposed RNN-SDR employs a minimum number of features when compared with other models. In addition, the experiments also validated that the RNN-SDR model does not significantly affect network performance in comparison with other options. Based on the analysis of the results of our experiments, we conclude that the RNN-SDR model is a promising approach for intrusion detection in SDN environments.
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Laftah Al-Yaseen, Wathiq, Zulaiha Ali Othman, and Mohd Zakree Ahmad Nazri. "Hybrid ModifiedK-Means with C4.5 for Intrusion Detection Systems in Multiagent Systems." Scientific World Journal 2015 (2015): 1–14. http://dx.doi.org/10.1155/2015/294761.
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Presently, the processing time and performance of intrusion detection systems are of great importance due to the increased speed of traffic data networks and a growing number of attacks on networks and computers. Several approaches have been proposed to address this issue, including hybridizing with several algorithms. However, this paper aims at proposing a hybrid of modifiedK-means with C4.5 intrusion detection system in a multiagent system (MAS-IDS). The MAS-IDS consists of three agents, namely, coordinator, analysis, and communication agent. The basic concept underpinning the utilized MAS is dividing the large captured network dataset into a number of subsets and distributing these to a number of agents depending on the data network size and core CPU availability. KDD Cup 1999 dataset is used for evaluation. The proposed hybrid modifiedK-means with C4.5 classification in MAS is developed in JADE platform. The results show that compared to the current methods, the MAS-IDS reduces the IDS processing time by up to 70%, while improving the detection accuracy.
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Hu, Ying, Li Min Sun, Sheng Chen Yu, Jiang Lan Huang, Xiao Ju Wang, and Hui Guo. "Coal Mine Disaster Warning Internet of Things Intrusion Detection System Based on Back Propagation Neural Network Improved by Genetic Algorithms." Applied Mechanics and Materials 441 (December 2013): 343–46. http://dx.doi.org/10.4028/www.scientific.net/amm.441.343.
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In order to improve the detection rate of intruders in coal mine disaster warning internet of things, and to solve the problem that the back propagate neural network (BPNN) is invalid when these initial weight and threshold values of BPNN are chosen impertinently, Genetic Algorithms (GA) s characteristic of getting whole optimization value was combined with BPNNs characteristic of getting local precision value with gradient method. After getting an approximation of whole optimization value of weight and threshold values of BPNN by GA, the approximation was used as first parameter of BPNN, to train (educate) the BPNN again (in other words, learning). The educated BPNN was used to recognize intruders in internet of things. Experiment results shown that this method was useful and applicable, and the detection right rate of intruders was above 95% for the KDD CUP 1999 data set.
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Muhammad, Arif Wirawan, and Faza Alameka. "Integrasi Normalized Relative Network Entropy Dan Neural Network Backpropagation (BP) Untuk Deteksi Dan Peramalan Serangan DDOS." Jurnal Rekayasa Teknologi Informasi (JURTI) 1, no. 1 (June 10, 2017): 1. http://dx.doi.org/10.30872/jurti.v1i1.630.
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Distributed denial-of-service (DDoS) merupakan jenis serangan dengan volume dan intensitas DDoS terus meningkat dengan biaya mitigasi yang terus meningkat seiring berkembangnya skala organisasi. Penelitian ini memiliki tujuan untuk mengembangkan sebuah pendekatan baru untuk mendeteksi dan membentuk cluster jenis serangan DDoS, berdasarkan pada karakteristik aktivitas jaringan dengan mengintegrasikan metode Normalized Relative Network Entropy (NRNE) sebagai estimator awal terhadap anomali aktivitas jaringan, dan metode Neural Network Backpropagation (BP) sebagai fungsi supervised learning terhadap pola anomali berdasarkan output dari NRNE. Data training yang digunakan dalam adalah log file dari KDD Cup 1999 yang diterbitkan oleh DARPA. Untuk pengujian real-world attack, digunakan data yang diterbitkan oleh CAIDA 2007. Pengujian simulation attack digunakan software DDoS Generator. Pengujian normal traffic digunakan data CAIDA 2011. Adanya pendekatan baru dalam mendeteksi serangan DDoS, diharapkan bisa menjadi sebuah komplemen terhadap sistem IDS dalam meramalkan terjadinya serangan DDoS.
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Fegade, Saurabh, Amey Bhadkamka, Kamlesh Karekar, Jaikishan Jeshnani, and Vinayak Kachare. "Network Intrusion Detection System Using C4.5 Algorithm." Journal of Communications Technology, Electronics and Computer Science 10 (March 1, 2017): 15. http://dx.doi.org/10.22385/jctecs.v10i0.139.
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There is a great concern about the security of computer these days. The number of attacks has increased in a great number in the last few years, intrusion detection is the main source of information assurance. While firewalls can provide some protection, they fail to provide protection fully and they even need to be complemented with an intrusion detection system (IDS). A newer approach for Intrusion detection is data mining techniques.IDS system can be developed using individual algorithms like neural networks, clustering, classification, etc. The result of these systems is good detection rate and low false alarm rate. According to a recent study, cascading of multiple algorithms gives a way better performance than single algorithm. Single algorithm systems have a high alarm rate. Therefore, to solve this problem, a combination of different algorithms are required. In this research paper, we use the hybrid algorithm for developing the intrusion detection system. C4.5 Support Vector Machine (SVM) and Decision Tree combined to achieve high accuracy and diminish the false alarm rate. Intrusions can be classified into types like Normal, DOS, R2L and U2R.Intrusion detection with Decision trees and SVM were tested with benchmark standard NSL- KDD, which is the extended version of KDD Cup 1999 for intrusion detection (ID).
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Koryshev, Nikolay, Ilya Hodashinsky, and Alexander Shelupanov. "Building a Fuzzy Classifier Based on Whale Optimization Algorithm to Detect Network Intrusions." Symmetry 13, no. 7 (July 6, 2021): 1211. http://dx.doi.org/10.3390/sym13071211.
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The quantity of network attacks and the harm from them is constantly increasing, so the detection of these attacks is an urgent task in the information security field. In this paper, we investigate an approach to building intrusion detection systems using a classifier based on fuzzy rules. The process of creating a fuzzy classifier based on a given set of input and output data can be presented as a solution to the problems of clustering, informative features selection, and the parameters of the rule antecedents optimization. To solve these problems, the whale optimization algorithm is used. The performance of algorithms for constructing a fuzzy classifier based on this metaheuristic is estimated using the KDD Cup 1999 intrusion detection dataset. On average, the resulting classifiers have a type I error of 0.92% and a type II error of 1.07%. The obtained results are also compared with the results of other classifiers. The comparison shows the competitiveness of the proposed method.
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Wilson, Ryan, and Charlie Obimbo. "Improvements on Self-Organizing Feature Maps for User-to-Root and Remote-to-Local Network Intrusion Detection on the 1999 KDD Cup Dataset." International Journal for Information Security Research 2, no. 2 (June 1, 2012): 132–39. http://dx.doi.org/10.20533/ijisr.2042.4639.2012.0016.
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Raman, M. R. Gauthama, K. Kannan, S. K. Pal, and V. S. Shankar Sriram. "Rough Set-hypergraph-based Feature Selection Approach for Intrusion Detection Systems." Defence Science Journal 66, no. 6 (October 31, 2016): 612. http://dx.doi.org/10.14429/dsj.66.10802.
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Immense growth in network-based services had resulted in the upsurge of internet users, security threats and cyber-attacks. Intrusion detection systems (IDSs) have become an essential component of any network architecture, in order to secure an IT infrastructure from the malicious activities of the intruders. An efficient IDS should be able to detect, identify and track the malicious attempts made by the intruders. With many IDSs available in the literature, the most common challenge due to voluminous network traffic patterns is the curse of dimensionality. This scenario emphasizes the importance of feature selection algorithm, which can identify the relevant features and ignore the rest without any information loss. In this paper, a novel rough set κ-Helly property technique (RSKHT) feature selection algorithm had been proposed to identify the key features for network IDSs. Experiments carried using benchmark KDD cup 1999 dataset were found to be promising, when compared with the existing feature selection algorithms with respect to reduct size, classifier’s performance and time complexity. RSKHT was found to be computationally attractive and flexible for massive datasets.
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Jain, Pratik, and Divyansh Kumrawat. "Comparing the Result of KDD Cup 1999 Data by using K-mean Algorithm and make Density based Cluster in Intrusion Detection System by Removing the Count Attribute." International Journal of Computer Applications 175, no. 16 (September 17, 2020): 21–26. http://dx.doi.org/10.5120/ijca2020920661.
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Ren, Min, Peiyu Liu, Zhihao Wang, and Jing Yi. "A Self-Adaptive Fuzzyc-Means Algorithm for Determining the Optimal Number of Clusters." Computational Intelligence and Neuroscience 2016 (2016): 1–12. http://dx.doi.org/10.1155/2016/2647389.
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For the shortcoming of fuzzyc-means algorithm (FCM) needing to know the number of clusters in advance, this paper proposed a new self-adaptive method to determine the optimal number of clusters. Firstly, a density-based algorithm was put forward. The algorithm, according to the characteristics of the dataset, automatically determined the possible maximum number of clusters instead of using the empirical rulenand obtained the optimal initial cluster centroids, improving the limitation of FCM that randomly selected cluster centroids lead the convergence result to the local minimum. Secondly, this paper, by introducing a penalty function, proposed a new fuzzy clustering validity index based on fuzzy compactness and separation, which ensured that when the number of clusters verged on that of objects in the dataset, the value of clustering validity index did not monotonically decrease and was close to zero, so that the optimal number of clusters lost robustness and decision function. Then, based on these studies, a self-adaptive FCM algorithm was put forward to estimate the optimal number of clusters by the iterative trial-and-error process. At last, experiments were done on the UCI, KDD Cup 1999, and synthetic datasets, which showed that the method not only effectively determined the optimal number of clusters, but also reduced the iteration of FCM with the stable clustering result.
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Christyawan, Tomi Yahya, Ahmad Afif Supianto, and Wayan Firdaus Mahmudy. "Anomaly-based intrusion detector system using restricted growing self organizing map." Indonesian Journal of Electrical Engineering and Computer Science 13, no. 3 (March 1, 2019): 919. http://dx.doi.org/10.11591/ijeecs.v13.i3.pp919-926.
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<p><span>The rapid development of internet and network technology followed by malicious threats and attacks on networks and computers. Intrusion detection system (IDS) was developed to solve that problems. The development of IDS using machine learning is needed for classifying the attacks. One method of the classification is Self-Organizing Map (SOM). SOM able to perform classification and visualization in learning process to gain new knowledge. However, the SOM has less efficient in learning process when applied in Big Data. This study proposes Restricted Growing SOM method with clustering reference vector (RGSOM-CRV) and Parallel RGSOM-CRV to improve SOM efficiency in classification with accuracy consideration to solve Big Data problem. Growing process in RGSOM is restricted by maximum nodes and growing threshold, the reupdate weight process will update unused reference vector when map size already maximum, these two processes solve the consuming time of regular GSOM. From the results of this research against KDD Cup 1999 dataset, proposed method Parallel RGSOM-CRV able to give 91.86% accuracy, 20.58% false alarm rate, 95.32% recall or detection rate, and precision is 94.35% and time consuming is outperform than regular Growing SOM. This proposed method is very promising to handle big data problems compared with other methods.</span></p>
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Kumar, Gulshan, and Krishan Kumar. "Design of an Evolutionary Approach for Intrusion Detection." Scientific World Journal 2013 (2013): 1–14. http://dx.doi.org/10.1155/2013/962185.
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A novel evolutionary approach is proposed for effective intrusion detection based on benchmark datasets. The proposed approach can generate a pool of noninferior individual solutions and ensemble solutions thereof. The generated ensembles can be used to detect the intrusions accurately. For intrusion detection problem, the proposed approach could consider conflicting objectives simultaneously like detection rate of each attack class, error rate, accuracy, diversity, and so forth. The proposed approach can generate a pool of noninferior solutions and ensembles thereof having optimized trade-offs values of multiple conflicting objectives. In this paper, a three-phase, approach is proposed to generate solutions to a simple chromosome design in the first phase. In the first phase, a Pareto front of noninferior individual solutions is approximated. In the second phase of the proposed approach, the entire solution set is further refined to determine effective ensemble solutions considering solution interaction. In this phase, another improved Pareto front of ensemble solutions over that of individual solutions is approximated. The ensemble solutions in improved Pareto front reported improved detection results based on benchmark datasets for intrusion detection. In the third phase, a combination method like majority voting method is used to fuse the predictions of individual solutions for determining prediction of ensemble solution. Benchmark datasets, namely, KDD cup 1999 and ISCX 2012 dataset, are used to demonstrate and validate the performance of the proposed approach for intrusion detection. The proposed approach can discover individual solutions and ensemble solutions thereof with a good support and a detection rate from benchmark datasets (in comparison with well-known ensemble methods like bagging and boosting). In addition, the proposed approach is a generalized classification approach that is applicable to the problem of any field having multiple conflicting objectives, and a dataset can be represented in the form of labelled instances in terms of its features.
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Jain*, Pratik, Ravikant Kholwal, and Tavneet Singh Khurana. "Reducing the False Alarm Rate in Intrusion Detection System by Providing Authentication and Improving the Efficiency of Intrusion Detection System by using Filtered Clusterer Algorithm using Weka Tool." International Journal of Engineering and Advanced Technology 10, no. 4 (April 30, 2021): 134–42. http://dx.doi.org/10.35940/ijeat.d2413.0410421.
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An IDS supervises network traffic by searching for skeptical activities and previously determined threats and sends alerts when detected. In the current times, the splendors of Intrusion detection still prevail censorial in cyber safety, but maybe not as a lasting resolution. To study a plant, one must start with roots, so Cambridge dictionary defines an intrusion as "an occasion when someone goes into an area or situation where they're not wanted or expected to be". For understanding the article, we will characterize interruption as any network movement or unapproved framework identified with one or more PCs or networks. This is an interpretation of permissible use of a system attempting to strengthen his advantages to acquire more noteworthy access to the framework that he is at present endowed, or a similar client attempting to associate with an unapproved far-off port of a server. These are the interruptions which will cause from the surface world, a bothered ex-representative who was terminated recently, or from your reliable staff. In this proviso, the fair information is found as an attack when the case is a false positive. Here they are zeroing in on this issue with a representation and offering one answer for a similar issue. The KDD CUP 1999 informational index is utilized. Here we dropped the number of counts and considered the OTP authentication system. In the result of this test, it may be very well seen that on the off chance that a class has a higher number of checks, at that point this class is believed to be an anomaly class. In any case, it will be considered an oddity if the genuine individual is passing the edge esteem is considered an intruder. One arrangement is proposed to distinguish the genuine individual and to eliminate false positives
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Jain*, Pratik, Ravikant Kholwal, and Muskan Patidar. "To Decrease the Issue of False Alarm Rate by Providing Authentication & Thus Improving the Efficiency of Intrusion Detection System by Comparing the Result of Filtered Clusterer Algorithm & Make-Density Based Clustering Algorithm without Attribute Count." International Journal of Recent Technology and Engineering 10, no. 1 (May 30, 2021): 110–20. http://dx.doi.org/10.35940/ijrte.a5755.0510121.
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The Intrusion Detection System sends alerts when it detects doubtful activities while monitoring the network traffic and other known threats. In today’s time in the field of Cyber security Intrusion Detection is considered a brilliant topic that could be objective. But it might not remain objectionable for a longer period. For understanding Intrusion Detection, the meaning of Intrusion must be clear at first. According to the oxford’s learners dictionary “Intrusion is the act of entering a place that is private or where you may not be wanted”. For this article, here it defines intrusion as any un-possessed system or network festivity on one (or more) computer(s) or network(s). Here is the example of a faithful user trying to access the system taking more than the usual trial counts to complete his access to the particular account or trying to connect to an unauthorized remote port of a server. The ex-employee who was being fired lately can provoke intrusion or any authentic worker can also provoke intrusion or any other person from the outside world could perform it. In this clause, the average data is found as the attack which is considered as the case of false positive. In this paper, the main focus is on the illustration and a solution offered for the same problem. Here we are using the KDD CUP 1999 data set. According to the outcome, the anomaly class is the one that has a higher number of counts than this class. Even if it is the true user trying to get access but the outcome is an anomaly due to the high number of counts in the class. This paper introduces a solution for the detection of a true person and eradicates the false positive.
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Li, Li, and Ye Yuan. "Data Preprocessing for Network Intrusion Detection." Applied Mechanics and Materials 20-23 (January 2010): 867–71. http://dx.doi.org/10.4028/www.scientific.net/amm.20-23.867.
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Most of IDS(Intrusion Detection System) are very particular about data source which might be asked to be categorical data or need to be correctly labeled. Therefore, the data preprocessing is an indispensable part in intrusion detecting. KDD Cpu 1999 Dataset is usually used for experimental data. This paper briefly introduces the features and the structure of the KDD Cpu 1999 Dataset and presents the method of the data preprocessing at Intrusion Detection System based on the neural network clustering’s algorithm.
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Najar, Asma, Safaa G. Kumari, Khaled M. Makkouk, and Abderazzek Daaloul. "A Survey of Viruses Affecting Faba Bean (Vicia faba) in Tunisia Includes First Record of Soybean dwarf virus." Plant Disease 87, no. 9 (September 2003): 1151. http://dx.doi.org/10.1094/pdis.2003.87.9.1151b.
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A survey was conducted in April 2003 to identify viruses infecting faba bean (Vicia faba L.) in six regions (Beja, Bizerte, Cap-bon, Le Kef, Siliana, and Zaghouan) in Tunisia. A total of 292 faba bean samples with symptoms of viral infection (leaf rolling, yellowing, and mosaic) were collected. The samples were tested at the virology laboratory of the International Center for Agricultural Research in the Dry Areas (ICARDA), Syria, for 11 viruses using the tissue-blot immunoassay procedure (3). Specific rabbit polyclonal antisera were used to test for Chickpea chlorotic dwarf virus (CpCDV) (provided by H. J. Vetten, BBA, Braunschweig, Germany), Alfalfa mosaic virus (AMV), Bean yellow mosaic virus (BYMV), Broad bean mottle virus (BBMV), Broad bean stain virus (BBSV), Cucumber mosaic virus (CMV), and Pea seedborne mosaic virus (PSbMV) (ICARDA, Aleppo, Syria). In addition, four specific monoclonal antibodies were used to detect Bean leaf roll virus (BLRV) (4B10) (2), Beet western yellows virus (BWYV) (ATCC PVAS-647; American Type Culture Collection, Manassas, VA), Faba bean necrotic yellows virus (FBNYV) (3-2E9) (1), and Soybean dwarf virus (SbDV) (ATCC PVAS-650). Serological tests showed that BBMV, a beetle-transmitted and seedborne virus identified in 23.3% (68 samples) of the samples tested, was the most common. BLRV, FBNYV, BWYV, BYMV, SbDV, and PSbMV were detected in 56, 33, 31, 10, 5, and 1 sample(s) of 292 samples tested, respectively. AMV, BBSV, CMV, and CpCDV were not detected in any samples tested. In Tunisia, BLRV, BWYV, BYMV, FBNYV, and PSbMV have previously been reported in faba bean (4), but to our knowledge, this is the first record of SbDV affecting faba bean in Tunisia, where it was detected in two fields in the Cap-bon Region. In sodium dodecyl sulfate-polyacrylamide gel electrophoresis followed by western blots, extracts from SbDV-infected plants were observed to contain 23-kDa structural proteins, which reacted strongly with SbDV monoclonal antibodies. Transmission tests showed that the samples, which reacted with SbDV monoclonal antibodies, were transmitted to faba bean plants by the pea aphid (Acyrthosiphon pisum Harris) in a persistent manner. To our knowledge, this is the first report of SbDV naturally infecting faba bean in Tunisia and it could cause a serious problem to other leguminous crops grown in Tunisia, such as French bean and peas, which are hosts for the virus. References: (1) A. Franz and K. M. Makkouk Ann. Appl. Biol. 128:255, 1996. (2) L. Katul. Characterization by serology and molecular biology of bean leaf roll virus and faba bean necrotic yellows virus. PhD thesis. University of Gottingen, Gottingen, Germany, 1992. (3) K. M. Makkouk and A. Comeau. Eur. J. Plant Pathol. 100:71, 1994. (4) A. Najar et al. Phytopathol. Mediterr. 39:423, 2000.
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Basavappa, Srisaila, Stine F. Pedersen, Nanna K. Jørgensen, J. Clive Ellory, and Else K. Hoffmann. "Swelling-Induced Arachidonic Acid Release via the 85-kDa cPLA2 in Human Neuroblastoma Cells." Journal of Neurophysiology 79, no. 3 (March 1, 1998): 1441–49. http://dx.doi.org/10.1152/jn.1998.79.3.1441.
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Basavappa, Srisaila, Stine F. Pedersen, Nanna K. Jørgensen, J. Clive Ellory, and Else K. Hoffmann. Swelling-induced arachidonic acid release via the 85-kDa cPLA2 in human neuroblastoma cells. J. Neurophysiol. 79: 1441–1449, 1998. Arachidonic acid or its metabolites have been implicated in the regulatory volume decrease (RVD) response after hypotonic cell swelling in some mammalian cells. The present study investigated the role of arachidonic acid (AA) during RVD in the human neuroblastoma cell line CHP-100. During the first nine minutes of hypo-osmotic exposure the rate of 3H-arachidonic acid (3H-AA) release increased to 250 ± 19% (mean ± SE, n = 22) as compared with cells under iso-osmotic conditions. This release was significantly inhibited after preincubation with AACOCF3, an inhibitor of the 85-kDa cytosolic phospholipase A2 (cPLA2). This indicates that a PLA2, most likely the 85-kDa cPLA2 is activated during cell swelling. In contrast, preincubation with U73122, an inhibitor of phospholipase C, did not affect the swelling-induced release of 3H-AA. Swelling-activated efflux of 36Cl and 3H-taurine were inhibited after preincubation with AACOCF3. Thus the swelling-induced activation of cPLA2 may be essential for stimulation of both 36Cl and 3H-taurine efflux during RVD. As the above observation could result from a direct effect of AA or its metabolite leukotriene D4 (LTD4), the effects of these agents were investigated on swelling-induced 36Cl and 3H-taurine effluxes. In the presence of high concentrations of extracellular AA, the swelling-induced efflux of 36Cl and 3H-taurine were inhibited significantly. In contrast, addition of exogenous LTD4 had no significant effect on the swelling-activated 36Cl efflux. Furthermore, exogenous AA increased cytosolic calcium levels as measured in single cells loaded with the calcium sensitive dye Fura-2. On the basis of these results we propose that cell swelling activates phospholipase A2 and that this activation via an increased production of AA or some AA metabolite(s) other than LTD4 is essential for RVD.
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Takei, Y., K. Ando, and M. Kawakami. "Atrial natriuretic peptide in eel plasma, heart and brain characterized by homologous radioimmunoassay." Journal of Endocrinology 135, no. 2 (November 1992): 325–31. http://dx.doi.org/10.1677/joe.0.1350325.
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ABSTRACT A highly specific and sensitive radioimmunoassay has been developed for the measurement of eel atrial natriuretic peptide (ANP). The antiserum, raised against eel ANP-(1–27) did not cross-react with two other eel natriuretic peptides, i.e. eel ventricular natriuretic peptide and C-type natriuretic peptide (CNP), or with any mammalian ANPs, CNPs or brain natriuretic peptides so far identified. The minimal detectable amount was 0·39 fmol (0·90 pg)/tube with more than 99% confidence. Because of its high sensitivity, the radioimmunoassay makes it possible to measure eel ANP directly with only a few microlitres of plasma without extraction. Using the radioimmunoassay we found high levels of ANP in the atrium (11 ± 2 pmol/mg wet tissue, n = 8), and much lower levels in the ventricle (56 ±8 fmol/mg, n=8) and the brain (22±1 fmol/mg, n = 8) of eels. Eel plasma contained a large amount of ANP (247 ± 66 fmol/ml, n= 8) compared with the levels reported in mammals, although atrial levels are similar between eels and mammals. Gel-permeation chromography revealed that a major form of ANP stored in the eel atrium, ventricle and brain has a molecular mass of approximately 14 kDa but low molecular forms of about 3 kDa are predominant in eel plasma. A detailed analysis with reverse-phase high-performance liquid chromatography showed that a major molecular form circulating in eel plasma is ANP-(1–27). ANP-(1–27) was also detected in small amounts in the eel atrium, ventricle and brain. Journal of Endocrinology (1992) 135, 325–331
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Gao, Cunji, and Peter J. Newman. "PECAM-1 Inhibits Signaling Pathways That Amplify Platelet Aggregation." Blood 108, no. 11 (November 16, 2006): 1505. http://dx.doi.org/10.1182/blood.v108.11.1505.1505.
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Though protein-tyrosine kinases get most of the attention, there is growing evidence that serine/threonine kinases (S/TKs) also play an important role in amplifying platelet activation responses following exposure to low-dose platelet agonists. For example, granule secretion is markedly impaired in platelets from mice deficient in either isoform of the STKs Akt1 (Blood104:1703, 2004) or Akt2 (J Clin Invest113:441, 2004), and pharmacological inhibition of the mitogen-activated protein kinase, p38, results in greatly reduced platelet aggregation and secretion (JBC271:6586, 1996; Blood107:965, 2006). Similarly, the integrin-linked kinase, ILK, is an S/TK that has recently been implicated in regulating both integrin activation (Thromb Haemost88:115, 2002; J Thromb Haemost2:1443, 2004) and cytoskeletal reorganization (Biochem J365:79, 2002; BBRC297:1324, 2002). We and others have previously shown that PECAM-1, a 130 kDa member of the immunoglobulin (Ig)-superfamily expressed on the surface of circulating platelets, leukocytes, and at the intercellular junctions of all continuous endothelium, functions to suppress platelet activation, though the mechanism by which this occurs has not been defined. To determine whether PECAM-1 might suppress S/TK-mediated platelet activation pathways, wild-type and PECAM-1 deficient murine platelets were stimulated with the GPVI-specific agonist, collagen-related peptide (CRP), and the activation state of Akt, p38, and ILK assessed biochemically. As previously reported, platelets from PECAM-1-deficient mice aggregated to a significantly greater extent in response to low-dose CRP than did their wild-type, PECAM-1-positive counterparts. Immunoblot analysis using phospho-specific antibodies revealed that both Akt and p38 existed in a hyper-activated state in CRP-stimulated, PECAM-1-deficient platelets - becoming phosphorylated both earlier and more intensively during platelet aggregation. ILK was similarly hyperactive, as assessed by determining the phosphorylation state of its substrate, glycogen synthase kinase 3β (GSK3β). To determine whether the inhibitory effects of PECAM-1 on S/TK-mediated platelet activation were dependant on prior fibrinogen binding to the major platelet integrin, GPIIb-IIIa, platelets were activated with low-dose CRP in the presence or absence of 2 mM RGDW. Pretreatment of platelets with RGDW had no effect on the ability of PECAM-1 to suppress activation of Akt,. p38, or ILK. Taken together, these data suggest that PECAM-1 functions as an inhibitory receptor in platelets, at least in part, by suppressing the activity of at least three different S/TKs, and thereby their ability to amplify early platelet activation responses.
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Stefanovic, B., C. Hellerbrand, and D. A. Brenner. "Regulatory Role of the Conserved Stem-Loop Structure at the 5′ End of Collagen α1(I) mRNA." Molecular and Cellular Biology 19, no. 6 (June 1, 1999): 4334–42. http://dx.doi.org/10.1128/mcb.19.6.4334.
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ABSTRACT Three fibrillar collagen mRNAs, α1(I), α2(I), and α1(III), are coordinately upregulated in the activated hepatic stellate cell (hsc) in liver fibrosis. These three mRNAs contain sequences surrounding the start codon that can be folded into a stem-loop structure. We investigated the role of this stem-loop structure in expression of collagen α1(I) reporter mRNAs in hsc’s and fibroblasts. The stem-loop dramatically decreases accumulation of mRNAs in quiescent hsc’s and to a lesser extent in activated hsc’s and fibroblasts. The stem-loop decreases mRNA stability in fibroblasts. In activated hsc’s and fibroblasts, a protein complex binds to the stem-loop, and this binding requires the presence of a 7mG cap on the RNA. Placing the 3′ untranslated region (UTR) of collagen α1(I) mRNA in a reporter mRNA containing this stem-loop further increases the steady-state level in activated hsc’s. This 3′ UTR binds αCP, a protein implicated in increasing stability of collagen α1(I) mRNA in activated hsc’s (B. Stefanovic, C. Hellerbrand, M. Holcik, M. Briendl, S. A. Liebhaber, and D. A. Brenner, Mol. Cell. Biol. 17:5201–5209, 1997). A set of protein complexes assembles on the 7mG capped stem-loop RNA, and a 120-kDa protein is specifically cross-linked to this structure. Thus, collagen α1(I) mRNA is regulated by a complex interaction between the 5′ stem-loop and the 3′ UTR, which may optimize collagen production in activated hsc’s.
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Levin, Nissan, and Jacob Zahavi. "The economics of selection of mail orders Drs. Zahavi and Levin are the masterminds behind the development of AMOS, a customized predictive modeling system for the Franklin Mint in Philadelphia, and GainSmarts, a general purpose data mining system that is the two-time winner of the KDD-CUP competition for the best data mining tools (1997 and 1998) sponsored by the American Association for Artificial Intelligence." Journal of Interactive Marketing 15, no. 3 (2001): 53. http://dx.doi.org/10.1002/dir.1016.abs.
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Salvetti, A., A. Lilienbaum, Z. Li, D. Paulin, and L. Gazzolo. "Identification of a negative element in the human vimentin promoter: modulation by the human T-cell leukemia virus type I Tax protein." Molecular and Cellular Biology 13, no. 1 (January 1993): 89–97. http://dx.doi.org/10.1128/mcb.13.1.89.
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The vimentin gene is a member of the intermediate filament multigene family and encodes a protein expressed, in vivo, in all mesenchymal derivatives and, in vitro, in cell types of various origin. We have previously demonstrated that the expression of this growth-regulated gene could be trans activated by the 40-kDa Tax protein of HTLV-I (human T-cell leukemia virus type I) and that responsiveness to this viral protein was mediated by the presence of an NF-kappa B binding site located between -241 and -210 bp upstream of the mRNA cap site (A. Lilienbaum, M. Duc Dodon, C. Alexandre, L. Gazzolo, and D. Paulin, J. Virol. 64:256-263, 1990). These previous assays, performed with deletion mutants of the vimentin promoter linked to the chloramphenicol acetyltransferase gene, also revealed the presence of an upstream negative region between -529 and -241 bp. Interestingly, the inhibitory activity exerted by this negative region was overcome after cotransfection of a Tax-expressing plasmid. In this study, we further characterize the vimentin negative element and define the effect of the Tax protein on the inhibitory activity of this element. We first demonstrate that a 187-bp domain (-424 to -237 bp) behaves as a negative region when placed upstream either of the NF-kappa B binding site of vimentin or of a heterologous enhancer such as that present in the desmin gene promoter. The negative effect can be further assigned to a 32-bp element which is indeed shown to repress the basal or induced activity of the NF-kappa B binding site.(ABSTRACT TRUNCATED AT 250 WORDS)
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Salvetti, A., A. Lilienbaum, Z. Li, D. Paulin, and L. Gazzolo. "Identification of a negative element in the human vimentin promoter: modulation by the human T-cell leukemia virus type I Tax protein." Molecular and Cellular Biology 13, no. 1 (January 1993): 89–97. http://dx.doi.org/10.1128/mcb.13.1.89-97.1993.
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The vimentin gene is a member of the intermediate filament multigene family and encodes a protein expressed, in vivo, in all mesenchymal derivatives and, in vitro, in cell types of various origin. We have previously demonstrated that the expression of this growth-regulated gene could be trans activated by the 40-kDa Tax protein of HTLV-I (human T-cell leukemia virus type I) and that responsiveness to this viral protein was mediated by the presence of an NF-kappa B binding site located between -241 and -210 bp upstream of the mRNA cap site (A. Lilienbaum, M. Duc Dodon, C. Alexandre, L. Gazzolo, and D. Paulin, J. Virol. 64:256-263, 1990). These previous assays, performed with deletion mutants of the vimentin promoter linked to the chloramphenicol acetyltransferase gene, also revealed the presence of an upstream negative region between -529 and -241 bp. Interestingly, the inhibitory activity exerted by this negative region was overcome after cotransfection of a Tax-expressing plasmid. In this study, we further characterize the vimentin negative element and define the effect of the Tax protein on the inhibitory activity of this element. We first demonstrate that a 187-bp domain (-424 to -237 bp) behaves as a negative region when placed upstream either of the NF-kappa B binding site of vimentin or of a heterologous enhancer such as that present in the desmin gene promoter. The negative effect can be further assigned to a 32-bp element which is indeed shown to repress the basal or induced activity of the NF-kappa B binding site.(ABSTRACT TRUNCATED AT 250 WORDS)
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Blencowe, B. J., M. Carmo-Fonseca, S. E. Behrens, R. Luhrmann, and A. I. Lamond. "Interaction of the human autoantigen p150 with splicing snRNPs." Journal of Cell Science 105, no. 3 (July 1, 1993): 685–97. http://dx.doi.org/10.1242/jcs.105.3.685.
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An important goal of studies on pre-mRNA splicing is to identify factors that mediate the snRNP-snRNP and snRNP-pre-mRNA interactions that take place in the spliceosome. The U4/U6 snRNP is one of the four snRNPs that are subunits of spliceosomes. A rare patient autoimmune serum (MaS serum) has recently been identified that specifically immunoprecipitates U4/U6 snRNP from HeLa cell extracts through recognition of a 150 kDa autoantigen (p150) (Okano and Medsger, Journal of Immunology, 146, 535–542, 1991). Here we show that in addition to U4/U6 snRNP, p150 can also be detected associated with 20 S U5, U4/U6.U5 and 17 S U2 snRNPs, but not with U1 snRNP. In each particle p150 is present in sub-stoichiometric levels relative to the major snRNP proteins. We show that MaS serum selectively immunoprecipitates a sub-population of U4/U6 snRNPs in which the m3G-cap structure is masked and that p150 is preferentially associated with U6 snRNA in the U4/U6 particle. Anti-p150 antibodies show widespread nucleoplasmic staining, excluding nucleoli, with an elevated concentration in coiled bodies. This changes to a discrete punctate pattern when cells are treated with alpha-amanitin. Both the cytological and biochemical data indicate that the p150 autoantigen is a snRNP-associated factor in vivo. We also present biochemical evidence confirming that assembly of U4/U6 and U5 snRNPs into a U4/U6.U5 tri-snRNP particle is an integral step in the spliceosome assembly pathway. Addition of the purified U4/U6.U5 tri-snRNP restores splicing activity to inactivated HeLa nuclear extracts in which splicing had been inhibited by specific depletion of either the U4/U6 or U5 snRNPs.
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Tsurumi, Hisashi, Naoe Goto, Masao Takemura, Takeshi Hara, Michio Sawada, Toshiki Yamada, Senji Kasahara, et al. "Serum-Soluble Tumor Necrosis Factor Receptor 2 (sTNF-R2) Level Determines Clinical Outcome in Patients with Aggressive Non-Hodgkin’s Lymphoma." Blood 104, no. 11 (November 16, 2004): 3271. http://dx.doi.org/10.1182/blood.v104.11.3271.3271.
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Abstract The tumor necrosis factor (TNF) plays a key role in inflammatory processes, as this cytokine is one of the earliest to be produced in such a condition, and triggers the following cytokine cascade. In addition, the TNF and their receptor system are believed to play a key role in the growth, differentiation, and/or apoptosis of the malignant cells. As for TNF receptors, the two types, the 55 kDa (p55, TNFR; TNF-R1) and the 75 kDa (p75, TNFR; TNF-R2) are simultaneously expressed on many cells at different levels. The extracellular domains of these two receptors are released from the cell membrane by cleavage of TNF-Rs as soluble TNF-Rs (sTNF-R1, sTNF-R2). Reportedly the serum TNF-Rs level rise in patients with some malignancies. The aim of the present study was to assess the prognostic significance of serum sTNF-R in aggressive non-Hodgkin’s lymphoma (NHL). Consecutive 110 previously untreated patients with aggressive NHL (diffuse large B-cell lymphoma; 94, peripheral T-cell lymphoma; 16) prospectively participated in this study between 1997 and 2002. The patients were treated with 6–8 cycles of CHOP or THP-COP regimens. To evaluate serum levels of sTNF-Rs (p55; TNF-R1, p75; TNF-R2), venous blood samples were drawn from patients immediately before the initiation of treatment. Serum sTNF-R1 and sTNF-R2 were determined using a sandwich enzyme-linked immunosorbent assay (ELISA). In healthy control subjects, the median of serum sTNF-R1 and sTNF-R2 levels were 1.2 ng/ml (range 0.3–2.9) and 4.17 ng/ml (range 1.91–8.51), respectively. High serum sTNF-R level was associated with some poor prognostic factors and low complete remission (CR) rate. Patients with high sTNF-R1(4 ng/ml and over) and sTNF-R2 (15 ng/ml and over) at onset had significantly lower survival rates (5-year: 19%, 19%) than those with low sTNF-R1 (under 4 ng/ml) and sTNF-R2 (under 15 ng/ml) (62%, 69%), respectively (p<0.0005, p<0.0001). Multivariate analysis employing sTNF-R2 and some conventional prognostic factors demonstrated that sTNF-R2 and performance status for overall survival (OS) and sTNF-R2, sIL-2R, and LDH for event free survival (EFS) were significantly poor prognostic factors. As for TNFa, a serum TNFa level is not related with sTNF-R1 or sTNF-R2 level in aggressive NHL. In addition, serum TNFa level is not associated with OS and EFS. In conclusion, serum sTNF-R2 might be a significant prognostic factor for aggressive NHL and a useful tool for selecting the appropriate therapeutic strategy in the treatment of aggressive NHL. The most reliable prognostic factor and the best combination of some prognostic factors for aggressive NHL should be clarified in order to assist in selecting appropriate treatment.
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Freson, Kathleen, Veerle Labarque, Chantal Thys, Christine Wittevrongel, Rita De Vos, Richard Farndale, and Chris Van Geet. "Increased Bleeding Tendency in a Patient with Caffey Disease Due to a COL1A1 Mutation and a Defect in Platelet Morphology and Function." Blood 106, no. 11 (November 16, 2005): 736. http://dx.doi.org/10.1182/blood.v106.11.736.736.
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Abstract Caffey disease or infantile cortical hyperostosis is characterized by hyperirritability, acute inflammation of soft tissues, and profound alterations of the shape and structure of particularly the long bones. This autosomal dominant disorder in 4 unrelated families is caused by a similar missense mutation (R836C) in the gene for the alpha1 chain of type I collagen (COL1A1; Gentile et al, JCI, 2005). The precise link between this mutation and either the localized inflammation problems or the originally described thrombocytosis and easy bruising associated with Caffey disease (Lorber et al, 1979) was not evidenced. In the present study, we studied the platelets from a 6-year-old girl with Caffey disease due to a de novo COL1A1 R836C mutation because of her increased bleeding tendency. Apart from the typical skeletal deformities she also presented with easy bruising. This COL1A1 mutation modulates normal megakaryopoiesis since the patient presented with a relatively high number of platelets (+/−450.000/mL) and a decreased MPV (7-8 fL). All other hematological parameters were normal. Electron microscopy further revealed platelets with a proliferation of the dense tubular system, a pronounced open canalicular system and a reduced number of often smaller dense granules. Platelet ATP secretion was reduced after stimulation with 5 mg/ml Horm collagen (2 mM: nl 3-7 mM). The PFA100 response with either collagen/epinephrine or collagen/ADP was within the normal range. Aggregation studies were suggestive for a selective impairment of platelet activation to collagen since the patient platelets showed a reduced and retarded response towards Horm Collagen, convulxin and the collagen related peptide (CRP-XL) but a normal aggregation with ADP, U46019 and arachidonic acid. Membrane glycoprotein (GP) profiling by flow cytometry showed a normal antibody binding to integrin beta3, integrin alpha2beta1 and GPVI. Since R to C amino acid substitutions in COL1A1 are associated with an increased disulfide crosslinking within mutant collagen fibers (Gentile et al, JCI, 2005) but also with other cysteine-containing proteins as integrins, we hypothesized that the platelet integrins might be triggered when mature megakaryocytes are in contact with the collagen type I of the extracellular matrix of the patients bone marrow. Immunoblot analysis of platelet lysates from the patient indeed showed the presence of a 190 kDa COL1A1 band, which could not be detected in control samples. In addition, beta1 integrin could be co-immunoprecipitated with an anti-COL1A1 antibody in platelet lysates from the patient. Further studies are needed to determine whether this COL1A1 R836C binding to platelet collagen receptors is responsible for the defective collagen signaling in this patient by receptor desensitization. In conclusion, we here present the first collagen type I mutation that leads to a defective platelet ultra structure and function.
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Khan, T., N. Cleaton, and T. Sheeran. "THU0594 A CASE OF TAKAYASU’S ARTERITIS IN A PATIENT WITH TUBERCULOUS LYMPHADENITIS." Annals of the Rheumatic Diseases 79, Suppl 1 (June 2020): 539.3–539. http://dx.doi.org/10.1136/annrheumdis-2020-eular.6542.
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Background:Takayasu’s arteritis (TA) is a large vessel vasculitis that principally affects the aorta and its main branches. The incidence has been reported at between 1.2 – 2.3 cases per million per year, more commonly in the Asian population. The age of onset is typically between tenth and fourth decade; between 80 and 90 percent of the cases are female.The relationship between Mycobacterium Tuberculosis (mTB) and TA has long been considered; both demonstrate chronic inflammatory changes on histological examination and some granuloma formation in arterial walls. There is increasing evidence implicating mTB in the pathogenesis of TA through molecular mimicry between the mycobacterium heat shock protein -65 (mHSP-65) and the human homologue HSP -60 (hHSP-60). However, no definitive link between the two diseases has been explained.Objectives:Case presentation.Results:A 23-year-old lady was referred to our outpatient rheumatology clinic with a twelve-month history of persistently enlarged cervical lymph nodes on the left side for which she had received six months of anti-Tuberculosis medication. She had been referred to the respiratory physicians who had diagnosed presumed Tuberculous Lymphadenitis, with caseating granulomas demonstrated on biopsy, positive acid-fast bacilli smear but a negative culture. The patient had been initiated six months of anti-Tuberculosis medication; however, her lymphadenopathy showed no improvement. More recently she described a five-month history of weakness, paraesthesia and claudication symptoms in her left upper limb with episodes of dizziness and blurred vision, episodes occurring 2-3 times per day and lasting between a few minutes to a few hours.Her examination at this presentation revealed an unrecordable blood pressure in the left upper limb and 104/67mmHg in the right. There was significant tender lymphadenopathy of the left cervical lymph nodes and diminished pulses in the left upper limb. Right sided pulses were normal. The rest of her examination was normal.Investigations at presentation revealed elevated inflammatory markers with C- reactive protein (CRP) of 116mg/dL and erythrocyte sedimentation rate (ESR) of 128mm/h. Complete blood count (CBC) found her to be anaemic with a haemoglobin of 100g/L, with a mean cell volume of 71.3fl, and have elevated platelet count of 649x 109/L. Recent computerized tomography scan with contrast of the thorax demonstrated features consistent with Takayasu Arteritis. Marked left subclavian stenosis was found on magnetic resonance imaging. High dose prednisolone at 60mg once daily along with Azathioprine 2mg/kg/day was started with a follow up appointment in two weeks.Conclusion:There is increasing evidence implicating mTB in the development of TA and a few cases recognising this link have been reported. We report a case of TA in a patient recently diagnosed and treated for Tuberculous lymphadenitis who then developed symptoms of TA. There should be a low threshold for suspecting a diagnosis of Takayasu’s arteritis in patients previously or actively infected with Mycobacterium Tuberculosis. Further research exploring the relationship between mTB and TA is required.References:[1]Espinoza JL, Ai S, Matsumura I. New Insights on the Pathogenesis of Takayasu Arteritis: Revisiting the Microbial Theory. Pathogens. 2018;7(3):73.[2]Aggarwal A, Chag M, Sinha N, et al. Takayasu’s arteritis: role of Mycobacterium tuberculosis and its 65 kDa heat shock protein. International Journal of Cardiology. 1996; 55: 49–55.[3]Reshkova V, Kalinova D, Rashkov R. Takayasu’s Arteritis associated with Tuberculosis Infections. Journal of Neurology and Neuroscience. 2016; 3:114.[4]Moritz K, Jansson Hilte F, Antje Kangowski, Christian Kneitz, Emil C. Reisinger. Tuberculosis and Takayasu arteritis: case-based review Rheumatology International 2019 39:345–351[5]D Misra, A Wakhlu, V Agarwal, D Danda. Recent advances in the management of Takayasu arteritis International Journal of Rheumatic Diseases 2019; 22: 60–68Disclosure of Interests:None declared
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Wang, W., S. Yang, Z. Yu, M. Wei, L. Zhong, and H. Song. "AB1067 CASE OR FAMILY?FROM 2 CHINESE FCAS3 CHILDREN WITH PLCG2 MUTATION TO THEIR FAMILIES." Annals of the Rheumatic Diseases 79, Suppl 1 (June 2020): 1822.3–1823. http://dx.doi.org/10.1136/annrheumdis-2020-eular.862.
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Background:Familial cold autoinflammatory syndrome 3 (FCAS3) is an autoinflammatory disease (AID) caused by mutation of the PLCG2 gene, which has not been reported in China. We will report 2 cases of Chinese FCAS3 patients with no claimed family history, but we found the same mutations in a parent during their genetic analysis. After further inquiry of the parent’s medical history, we confirmed that actually, they were two FCAS3 families. Through a literature review, we found that the clinical features of Chinese patients are milder than foreign countries, and their symptoms are concealed and may be ignored, resulting in mistakes in family history collecting.Objectives:To summarize the genetic and clinical features of Chinese FCAS3 patients and to provide diagnostic recommendations for the disease.Methods:Two suspected AID children with recurrent fever and urticaria were enrolled in this study. Clinical data and family history were collected, and genetic analysis was performed by next-generation sequencing (PID panel or WES) and Sanger-based validation. Literature was reviewed from PubMed, CNKI, and Wanfang Database.Results:The two children were both diagnosed to be FCAS3 with PLCG2 mutation. The clinical manifestations of 2 children were recurrent fever, urticaria, and increased ESR and CRP. Case 1 has a paternal, and Case 2 has a maternal heterozygous mutation in the PLCG2 gene, while both had claimed without a family history. Further inquiry showed the two parents used to have a fever with urticaria. By comparing with foreign literature, we found our patients were milder than abroad patients. Large fragment deletions were relatively more common in foreign patients.Conclusion:We reported the case of FCAS3 in China for the first time. Their genotype and phenotype were different from foreign patients. Their symptoms are mild, and heterozygous mutations are more common than foreign patients, which are the main differences. The difference in mutation type may be the reason for different clinical manifestations. Besides, both two families showed a trend of more severe clinical features in the next generation. As the symptoms of the elders were not obvious and may be ignored, it caused trouble for the genetic diagnosis. Therefore, family history should be collected carefully. For rashes and fevers, which are not too severe in overall symptoms, care should be taken about the possibility of AIDs. Genetic testing can help to make a definite diagnosis.Table 1.Descriptive charecteristics of the patients with FMF, n=474VariableCompliant(n=230)Noncompliant (n=244)P valueGender of patient (F)142(61.7)147(60.2)0.73Age, years*35(28-42.5)34(27-44.2)0.88Being Married152(66.1)146(59.8)0.15Disease duration, years*22(14-31)22(15-31)0.71Number of index flare*within last 12-month6.7(1-10)5(3-10)<0.001Family historyof parents54(23.5)39(16.0)0.04Family historyof sibling73(32.9)102(43.4)0.02Comorbid disease presence73(31.7)55(22.5)0.02Treatment<0.001Colchicine230 (94.1)180(78.6)Anakinra&Canakinumab134(5.3)49(21.4)Colchicine response presence127(55.2)126(52.3)0.52Drug using except FMF74(32.2)44(18.0)<0.001Presence of 2 attacks except fever90 (39.1)68(27.9)0.009Chronic peripheral arthritis16(7.0)7(2.9)0.03Amyloidosis18(7.8)9(3.7)0.05Proteinuria23(10.8)8(3.6)0.004Adequate medical care161(70.0)132(54.8)<0.001ISSF severity score*3(2-4)3(2-4)0.02ADDI index*1(0-1)1(0-1)0.05References:[1]Pathak S, Mcdermott M F, Savic S. Autoinflammatory diseases: update on classification diagnosis and management[J]. Journal of Clinical Pathology, 2017, 70(1):1-8.[2]Broderick, L., Hereditary Autoinflammatory Disorders: Recognition and Treatment. Immunol Allergy Clin North Am, 2019. 39(1):13-29.[3]Milner, Joshua D. PLAID: A Syndrome of Complex Patterns of Disease and Unique Phenotypes[J]. Journal of Clinical Immunology, 2015, 35(6):527-530.[4]Picard C, Gaspar H B, Al-Herz W, et al. International Union of Immunological Societies: 2017 Primary Immunodeficiency Diseases Committee Report on Inborn Errors of Immunity[J]. Journal of Clinical Immunology, 2017, 38(Suppl 1):96-128.[5]Ombrello M J, Remmers E F, Sun G, et al. Cold Urticaria, Immunodeficiency, and Autoimmunity Related to PLCG2 Deletions[J]. New England Journal of Medicine, 2012, 366(4):330-8.[6]Zhou Q, Lee GS, Brady J, et al. A Hypermorphic Missense Mutation in PLCG2, Encoding Phospholipase Cγ2, Causes a Dominantly Inherited Autoinflammatory Disease with Immunodeficiency[J]. American Journal of Human Genetics, 2012, 91(4).[7]Neves, J.F., et al., Novel PLCG2 Mutation in a Patient with APLAID and Cutis Laxa. Front Immunol, 2018. 9: 2863.[8]Mcdermott M F, Aksentijevich I, Galon J, et al. Germline mutations in the extracellular domains of the 55 kDa TNF receptor, TNFR1, define a family of dominantly inherited autoinflammatory syndromes[J]. Cell, 1999, 97(1):133-144.Disclosure of Interests:None declared
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H. Jebur, Hamid, Mohd Aizaini Maarof, and Anazida Zainal. "Defining Generic Attributes for IDS Classification." Jurnal Teknologi 74, no. 1 (April 12, 2015). http://dx.doi.org/10.11113/jt.v74.1375.
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Detection accuracy of Intrusion Detection System (IDS) depends on classifying network traffic based on data features. Using all features for classification consumes more computation time and computer resources. Some of these features may be redundant and irrelevant therefore, they affect the detection of traffic anomalies and the overall performance of the IDS. The literature proposed different algorithms and techniques to define the most relevant sets of features of KDD cup 1999 that can achieve high detection accuracy and maintain the same performance as the total data features. However, all these algorithms and techniques did not produce optimal solutions even when they utilized same datasets. In this paper, a new approach is proposed to analyze the researches that have been conducted on KDD cup 1999 for features selection to define the possibility of determining effective generic features of the common dataset KDD cup 1999 for constructing an efficient classification model. The approach does not rely on algorithms, which shortens the computational cost and reduces the computer resources. The essence of the approach is based on selecting the most frequent features of each class and all classes in all researches, then a threshold is used to define the most significant generic features. The results revealed two sets of features containing 7 and 8 features. The classification accuracy by using eight features is almost the same as using all dataset features.
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Vijaya Rani, S., and G. N. K. Suresh Babu. "Intrusion Detection Using Back Propagation Neural Network and Quick Reduct Algorithms." International Journal of Scientific Research in Computer Science, Engineering and Information Technology, December 9, 2018, 317–25. http://dx.doi.org/10.32628/cseit183891.
Full textAbstract:
It is a big challenge to safeguard a network and data due to various network threats and attacks in a network system. Intrusion detection system is an effective technique to negotiate the issues of network security by utilizing various network classifiers. It detects malicious attacks. The data sets available in the study of intrusion detection system were DARPA, KDD 1999 cup, NSL_KDD, DEFCON, ISCX-UNB, KDD 1999 cup data set is the best and old data set for research purpose on intrusion detection. The data is preprocessed, normalized and trained by BPN algorithm. Further the normalized data is discretized using Entropy discretization and feature selection carried out by quick reduct methods. After feature selection, the concerned feature from normalized data is processed through BPN for better accuracy and efficiency of the system.
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Golovko, Vladimir, and Pavel Kochurko. "INTRUSION RECOGNITION USING NEURAL NETWORKS." International Journal of Computing, August 1, 2014, 37–42. http://dx.doi.org/10.47839/ijc.4.3.360.
Full textAbstract:
Intrusion detection techniques are of great importance for computer network protecting because of increasing the number of remote attack using TCP/IP protocols. There exist a number of intrusion detection systems, which are based on different approaches for anomalous behavior detection. This paper focuses on applying neural networks for attack recognition. It is based on multilayer perceptron. The 1999 KDD Cup data set is used for training and testing neural networks. The results of experiments are discussed in the paper.
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Bharti, Kusum Kumari, Sanyam Shukla, and Sweta Jain. "Intrusion detection using clustering." International Journal of Computer and Communication Technology, October 2010, 248–55. http://dx.doi.org/10.47893/ijcct.2010.1052.
Full textAbstract:
In increasing trends of network environment every one gets connected to the system. So there is need of securing information, because there are lots of security threats are present in network environment. A number of techniques are available for intrusion detection. Data mining is the one of the efficient techniques available for intrusion detection. Data mining techniques may be supervised or unsuprevised.Various Author have applied various clustering algorithm for intrusion detection, but all of these are suffers form class dominance, force assignment and No Class problem. This paper proposes a hybrid model to overcome these problems. The performance of proposed model is evaluated over KDD Cup 1999 data set.
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"Effective Parameter Optimization & Classification using Bat-Inspired Algorithm with Improving NSSA." International Journal of Engineering and Advanced Technology 9, no. 1 (October 30, 2019): 3343–49. http://dx.doi.org/10.35940/ijeat.a1498.109119.
Full textAbstract:
Network Security is an important aspectin communication-related activities. In recent times, the advent of more sophisticated technologies changed the way the information is being sharedwith everyone in any part of the world.Concurrently, these advancements are mishandled to compromise the end-user devices intentionally to steal their personal information. The number of attacks made on targeted devices is increasing over time. Even though the security mechanisms used to defend the network is enhanced and kept updated periodically, new advanced methods are developed by the intruders to penetrate the system. In order to avoid these discrepancies, effective strategies must be applied to enhance the security measures in the network. In this paper, a machine learning-based approach is proposed to identify the pattern of different categories of attacks made in the past. KDD cup 1999 dataset is accessed to develop this predictive model. Bat optimization algorithm identifies the optimal parameter subset. Supervised machine learning algorithms were employed to train the model from the data to make predictions. The performance of the system is evaluated through evaluation metrics like accuracy, precision and so on. Four classification algorithms were used out of which, gradient boosting model outperformed the benchmarked algorithms and proved its importance on data classification based on the accuracy obtained from this model.
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Jeyakarthic, M., and A. Thirumalairaj. "Binary Grasshopper Optimization Based Feature Selection For Intrusion Detection System Using Feed Forward Neural Network Classifier." Recent Advances in Computer Science and Communications 13 (May 29, 2020). http://dx.doi.org/10.2174/2666255813999200529110158.
Full textAbstract:
Background: Due to the advanced improvement in internet and network technologies, significant number of intrusions and attacks takes place. An intrusion detection system (IDS) is employed to prevent distinct attacks. Several machine learning approaches has been presented for the classification of IDS. But, IDS suffer from the curse of dimensionality that results to increased complexity and decreased resource exploitation. Consequently, it becomes necessary that significant features of data must be investigated by the use of IDS for reducing the dimensionality. Aim: In this article, a new feature selection (FS) based classification system is presented which carries out the FS and classification processes. Methods: Here, the binary variants of the Grasshopper Optimization Algorithm called BGOA is applied as a FS model. The significant features are integrated using an effective model to extract the useful ones and discard the useless features. The chosen features are given to the feed forward neural network (FFNN) model to train and test the KDD99 dataset. Results: The validation of the presented model takes place using a benchmark KDD Cup 1999 dataset. By the inclusion of FS process, the classifier results gets increased by attaining FPR of 0.43, FNR of 0.45, sensitivity of 99.55, specificity of 99.57, accuracy of 99.56, Fscore of 99.59 and kappa value of 99.11. Conclusion: The experimental outcome ensured the superior performance of the presented model compared to diverse models under several aspects and is found to be an appropriate tool for detecting intrusions.
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Gong, Yiguang, Yunping Liu, and Chuanyang Yin. "A novel two-phase cycle algorithm for effective cyber intrusion detection in edge computing." EURASIP Journal on Wireless Communications and Networking 2021, no. 1 (July 10, 2021). http://dx.doi.org/10.1186/s13638-021-02016-z.
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AbstractEdge computing extends traditional cloud services to the edge of the network, closer to users, and is suitable for network services with low latency requirements. With the rise of edge computing, its security issues have also received increasing attention. In this paper, a novel two-phase cycle algorithm is proposed for effective cyber intrusion detection in edge computing based on a multi-objective genetic algorithm (MOGA) and modified back-propagation neural network (MBPNN), namely TPC-MOGA-MBPNN. In the first phase, the MOGA is employed to build a multi-objective optimization model that tries to find the Pareto optimal parameter set for MBPNN. The Pareto optimal parameter set is applied for simultaneous minimization of the average false positive rate (Avg FPR), mean squared error (MSE) and negative average true positive rate (Avg TPR) in the dataset. In the second phase, some MBPNNs are created based on the parameter set obtained by MOGA and are trained to search for a more optimal parameter set locally. The parameter set obtained in the second phase is used as the input of the first phase, and the training process is repeated until the termination criteria are reached. A benchmark dataset, KDD cup 1999, is used to demonstrate and validate the performance of the proposed approach for intrusion detection. The proposed approach can discover a pool of MBPNN-based solutions. Combining these MBPNN solutions can significantly improve detection performance, and a GA is used to find the optimal MBPNN combination. The results show that the proposed approach achieves an accuracy of 98.81% and a detection rate of 98.23% and outperform most systems of previous works found in the literature. In addition, the proposed approach is a generalized classification approach that is applicable to the problem of any field having multiple conflicting objectives.
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Bartaševičius, Linas, Albertas Skurvydas, Ramutis Kairaitis, and Šarūnas Sakalauskas. "Vienkartinių dinaminių ir izometrinių pratybų poveikis keturgalvio šlaunies raumens susitraukimo ir atsipalaidavimo savybėms." Baltic Journal of Sport and Health Sciences 4, no. 75 (October 29, 2018). http://dx.doi.org/10.33607/bjshs.v4i75.406.
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Tyrimo metu norėta išsiaiškinti, kaip dėl vienkartinių izometrinių ir dinaminių pratybų, skirtų staigiajai jėgai lavinti, kinta kojų raumenų valingo ir nevalingo susitraukimo savybės, raumenų atsparumas nuovargiui. Tiriamąją imtį sudarė 16 nesportuojančių suaugusių vyrų. Per izometrines staigiosios jėgos ugdymo pratybas buvo prašoma per 5 sekundes 5 kartus pasiekti didžiausią valingąją jėgą, paskui visiškai atpalaiduoti raumenį. Po šio krūvio buvo daroma 5 min pertrauka ir serija kartojama iš naujo. Iš viso buvo atliekama 12 tokių krūvio serijų.Per dinamines staigiosios jėgos pratybas tiriamieji mynė veloergometrą. Mechaninis veloergometro priešinimasis sudarė 7,5% tiriamojo kūno masės. Pratybų metu tiriamieji turėjo pasiekti maksimalų mynimo dažnumą, po kurio buvo staigiai padidinamas pasipriešinimas. Atsiradus pasipriešinimui, tiriamieji turėjo stengtis 5 sekundes išlaikyti pasiektą maksimalų mynimo dažnumą. Tada buvo daroma 5 min pertrauka, ir tai sudarė vieną krūvio seriją. Iš viso buvo atliekama 12 tokių serijų.Valingo ir nevalingo raumenų susitraukimo savybės buvo nustatomos tiriamiesiems sėdint specialioje kėdėje. Ties apatiniu dešinės kojos blauzdos trečdaliu buvo užjuosiamas diržas, per traukę sujungtas su metaliniu žiedu, ant kurio užklijuotas tenzodaviklis. Nuo keturgalvio šlaunies raumens susitraukimo jėgos priklausydavo metalinio žiedo defor-macija, kurią tenzodaviklis paversdavo elektros signalu. Šio signalo kitimo dydis buvo tiesiog proporcingas metalinį žiedą deformuojančios jėgos dydžiui. Signalas iš tenzodaviklio buvo perduodamas į stiprintuvą, paskui per plokštę „Analogas—kodas“ — į personalinį kompiuterį. Buvo matuojama susitraukimo jėga, sukelta stimuliuojant raumenį 1, 10, 20 ir 50 Hz dažnumu, ir maksimali valingoji jėga.Naudojant tiesioginę elektrostimuliaciją, ant keturgalvio šlaunies raumens distalinio ir proksimalinio trečdalių buvo dedami paviršiniai 9 × 18 cm metaliniai elektrodai. Elektrodai buvo sujungiami su elektrostimuliatoriumi, įmontuotu į elektromiografą „Medicor MG440“. Raumuo buvo dirginamas stačiakampės formos elektriniu impulsu arba jų serija. Elektrostimuliatoriaus siunčiamų dirgiklių (impulsų serijos) dažnumas ir keturgalvio šlaunies raumens su-sitraukimo jėga buvo registruojami IBM tipo personaliniame kompiuteryje (CPU i486-33MHz, RAM 8M) „Stimula Lab“ programa (programos kūrėjas E. Povilonis, 1994). Tyrimo duomenys parodė, kad po dinaminių ir izometrinių pratybų raumenų susitraukimo (Ct) ir atsipalaidavimo (Rt) savybės statistiškai patikimai nepakito (p > 0,05), stimuliuojant raumenį P 1, P 20, P 50 Hz dažniu. Abiem tyrimo atvejais pasireiškė mažų dažnių nuovargis. Tyrimo rezultatai parodė, kad didžiausi keturgalvio šlaunies raumens su-sitraukimo ir atsipalaidavimo savybių pokyčiai pastebimi po dinaminių staigiosios jėgos ugdymo pratybų. Dinaminės pratybos sukelia didesnį raumenų nuovargį, negu staigiosios jėgos lavinimo izometrinės pratybos.Raktažodžiai: staigioji jėga, izometrinės pratybos, dinaminės pratybos, raumenų nuovargis.