Готові списки джерел за темами / KDD Cup 1999

Добірка наукової літератури з теми "KDD Cup 1999"

Автор: Grafiati

Опубліковано: 30 травня 2022

Оновлено: 31 травня 2022

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Статті в журналах з теми "KDD Cup 1999"

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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Анотація:

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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Анотація:

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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Анотація:

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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Більше джерел

Дисертації з теми "KDD Cup 1999"

Вашеняк, Артем Миколайович. "Методи виявлення прихованих атак на інформаційні системи із застосуванням нечіткої логіки". Магістерська робота, Хмельницький національний університет, 2021. http://elar.khnu.km.ua/jspui/handle/123456789/11104.

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Метою кваліфікаційної роботи є розробка комп’ютерної системи на основі нечіткої логіки для виявлення прихованих атак. Дана кваліфікаційна робота присвячена удосконаленню методу реалізації систем ідентифікації вторгнень на основі нечіткої логіки.

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Книги з теми "KDD Cup 1999"

Červinka, Stanislav. Kdo zabil Arconu? Praha: Tempo, 1991.

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Find and Fit Baby Animals: (Quarto 1999). Burnaby, Canada: Ben's Books, 1999.

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Частини книг з теми "KDD Cup 1999"

Wang, Yun, and Lee Seidman. "Risk Factors to Retrieve Anomaly Intrusion Information and Profile User Behavior." In Information Security and Ethics, 2407–21. IGI Global, 2008. http://dx.doi.org/10.4018/978-1-59904-937-3.ch159.

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The use of network traffic audit data for retrieving anomaly intrusion information and profiling user behavior has been studied previously, but the risk factors associated with attacks remain unclear. This study aimed to identify a set of robust risk factors via the bootstrap resampling and logistic regression modeling methods based on the KDD-cup 1999 data. Of the 46 examined variables, 16 were identified as robust risk factors, and the classification showed similar performances in sensitivity, specificity, and correctly classified rate in comparison with the KDD-cup 1999 winning results that were based on a rule-based decision tree algorithm with all variables. The study emphasizes that the bootstrap simulation and logistic regression modeling techniques offer a novel approach to understanding and identifying risk factors for better information protection on network security.

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Zhang, Ji. "A Subspace-Based Analysis Method for Anomaly Detection in Large and High-Dimensional Network Connection Data Streams." In Data Mining, 530–49. IGI Global, 2013. http://dx.doi.org/10.4018/978-1-4666-2455-9.ch026.

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A great deal of research attention has been paid to data mining on data streams in recent years. In this chapter, the authors carry out a case study of anomaly detection in large and high-dimensional network connection data streams using Stream Projected Outlier deTector (SPOT) that is proposed in (Zhang et al. 2009) to detect anomalies from data streams using subspace analysis. SPOT is deployed on the 1999 KDD CUP anomaly detection application. Innovative approaches for training data generation, anomaly classification, and false positive reduction are proposed in this chapter as well. Experimental results demonstrate that SPOT is effective and efficient in detecting anomalies from network data streams and outperforms existing anomaly detection methods.

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Zhang, Ji. "A Dynamic Subspace Anomaly Detection Method Using Generic Algorithm for Streaming Network Data." In Handbook of Research on Emerging Developments in Data Privacy, 403–25. IGI Global, 2015. http://dx.doi.org/10.4018/978-1-4666-7381-6.ch018.

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A great deal of research attention has been paid to data mining on data streams in recent years. In this chapter, the authors carry out a case study of anomaly detection in large and high-dimensional network connection data streams using Stream Projected Outlier deTector (SPOT) that is proposed in Zhang et al. (2009) to detect anomalies from data streams using subspace analysis. SPOT is deployed on 1999 KDD CUP anomaly detection application. Innovative approaches for training data generation, anomaly classification, false positive reduction, and adoptive detection subspace generation are proposed in this chapter as well. Experimental results demonstrate that SPOT is effective and efficient in detecting anomalies from network data streams and outperforms existing anomaly detection methods.

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Тези доповідей конференцій з теми "KDD Cup 1999"

Shah, Bhavin, and Bhushan H. Trivedi. "Reducing Features of KDD CUP 1999 Dataset for Anomaly Detection Using Back Propagation Neural Network." In 2015 Fifth International Conference on Advanced Computing & Communication Technologies (ACCT). IEEE, 2015. http://dx.doi.org/10.1109/acct.2015.131.

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Wilson, Ryan, and Charlie Obimbo. "Self-organizing feature maps for User-to-Root and Remote-to-Local network intrusion detection on the KDD Cup 1999 dataset." In 2011 World Congress on Internet Security (WorldCIS-2011). IEEE, 2011. http://dx.doi.org/10.1109/worldcis17046.2011.5749879.

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Fank, Elias Augusto, Geomar A. Schreiner, and Denio Duarte. "Estudo comparativo de plataformas de Deep Learning: Apache Singa, Graphlab e H2O." In Escola Regional de Banco de Dados. Sociedade Brasileira de Computação - SBC, 2021. http://dx.doi.org/10.5753/erbd.2021.17234.

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Técnicas de Deep learning vêm mostrando avanços em várias tarefas de aprendizado de máquina. Porém a implementação dessas técnicas é muito complexa. Assim, para ajudar na implementação de projetos de Deep Learning, plataformas estão sendo criados. Já existe uma quantidade considerável destas plataformas disponível. Isso acaba trazendo uma dificuldade na escolha de quem procura começar um projeto. Com o objetivo de auxiliar nesta escolha, este trabalho faz um estudo comparativo entre algumas plataformas: Apache Singa, Graphlab e H2O. Experimentos são conduzidos utilizando os conjunto de dados MNIST e KDD Cup 1999. Resultados apontam que as plataformas testadas têm suas vantagens: Graphlab é a mais intuitiva, a Apache Singa oferece mais recursos e H2O obteve os melhores resultados de predição.

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Звіти організацій з теми "KDD Cup 1999"

Epel, Bernard L., Roger N. Beachy, A. Katz, G. Kotlinzky, M. Erlanger, A. Yahalom, M. Erlanger, and J. Szecsi. Isolation and Characterization of Plasmodesmata Components by Association with Tobacco Mosaic Virus Movement Proteins Fused with the Green Fluorescent Protein from Aequorea victoria. United States Department of Agriculture, September 1999. http://dx.doi.org/10.32747/1999.7573996.bard.

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Анотація:

The coordination and regulation of growth and development in multicellular organisms is dependent, in part, on the controlled short and long-distance transport of signaling molecule: In plants, symplastic communication is provided by trans-wall co-axial membranous tunnels termed plasmodesmata (Pd). Plant viruses spread cell-to-cell by altering Pd. This movement scenario necessitates a targeting mechanism that delivers the virus to a Pd and a transport mechanism to move the virion or viral nucleic acid through the Pd channel. The identity of host proteins with which MP interacts, the mechanism of the targeting of the MP to the Pd and biochemical information on how Pd are alter are questions which have been dealt with during this BARD project. The research objectives of the two labs were to continue their biochemical, cellular and molecular studies of Pd composition and function by employing infectious modified clones of TMV in which MP is fused with GFP. We examined Pd composition, and studied the intra- and intercellular targeting mechanism of MP during the infection cycle. Most of the goals we set for ourselves were met. The Israeli PI and collaborators (Oparka et al., 1999) demonstrated that Pd permeability is under developmental control, that Pd in sink tissues indiscriminately traffic proteins of sizes of up to 50 kDa and that during the sink to source transition there is a substantial decrease in Pd permeability. It was shown that companion cells in source phloem tissue export proteins which traffic in phloem and which unload in sink tissue and move cell to cell. The TAU group employing MP:GFP as a fluorescence probe for optimized the procedure for Pd isolation. At least two proteins kinases found to be associated with Pd isolated from source leaves of N. benthamiana, one being a calcium dependent protein kinase. A number of proteins were microsequenced and identified. Polyclonal antibodies were generated against proteins in a purified Pd fraction. A T-7 phage display library was created and used to "biopan" for Pd genes using these antibodies. Selected isolates are being sequenced. The TAU group also examined whether the subcellular targeting of MP:GFP was dependent on processes that occurred only in the presence of the virus or whether targeting was a property indigenous to MP. Mutant non-functional movement proteins were also employed to study partial reactions. Subcellular targeting and movement were shown to be properties indigenous to MP and that these processes do not require other viral elements. The data also suggest post-translational modification of MP is required before the MP can move cell to cell. The USA group monitored the development of the infection and local movement of TMV in N. benthamiana, using viral constructs expressing GFP either fused to the MP of TMV or expressing GFP as a free protein. The fusion protein and/or the free GFP were expressed from either the movement protein subgenomic promoter or from the subgenomic promoter of the coat protein. Observations supported the hypothesis that expression from the cp sgp is regulated differently than expression from the mp sgp (Szecsi et al., 1999). Using immunocytochemistry and electron microscopy, it was determined that paired wall-appressed bodies behind the leading edge of the fluorescent ring induced by TMV-(mp)-MP:GFP contain MP:GFP and the viral replicase. These data suggest that viral spread may be a consequence of the replication process. Observation point out that expression of proteins from the mp sgp is temporary regulated, and degradation of the proteins occurs rapidly or more slowly, depending on protein stability. It is suggested that the MP contains an external degradation signal that contributes to rapid degradation of the protein even if expressed from the constitutive cp sgp. Experiments conducted to determine whether the degradation of GFP and MP:GFP was regulated at the protein or RNA level, indicated that regulation was at the protein level. RNA accumulation in infected protoplast was not always in correlation with protein accumulation, indicating that other mechanisms together with RNA production determine the final intensity and stability of the fluorescent proteins.

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