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Journal articles on the topic 'KDD Cup 1999'

Author: Grafiati

Published: 30 May 2022

Last updated: 31 July 2025

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1

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 (2021): 123–32. http://dx.doi.org/10.29056/jncist.2021.04.02.

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Eniodunmo, Oluwapelumi, and Raid Al-Aqtash. "A Predictive Model to Predict a Cyberattack Using Self Normalizing Neural Networks." International Journal of Statistics and Probability 12, no. 6 (2023): 60. http://dx.doi.org/10.5539/ijsp.v12n6p60.

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A cyberattack is an unauthorized access and a threat to information systems. Intelligent intrusion systems rely on advancements in technology to detect cyberattacks. In this article, the KDD CUP 99 dataset, from the Third International Knowledge Discovery and Data mining Tools Competition that was held in 1999, is considered, and a class of neural networks, known as Self-Normalizing Neural Networks, is utilized to build a predictive model to detect cyberattacks in the KDD CUP 99 dataset. The accuracy and the precision of the self-normalizing neural network is compared with that of the k-neares

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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 minori

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Mathan, Pinaki Shashishekhar. "Intrusion Detection Using Machine Learning Classification and Regression." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 03 (2025): 1–9. https://doi.org/10.55041/ijsrem42130.

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An Intrusion Detection System (IDS) is a crucial security mechanism designed to protect computer networks from unauthorized access and cyber threats. With the rapid expansion of Internet-based data transmission, ensuring network security has become increasingly challenging. IDS continuously monitors and analyzes network traffic to detect malicious activities, relying on datasets like KDD Cup 1999 for training and evaluation. Effective IDS development involves preprocessing steps such as feature selection, normalization, and addressing data imbalance to enhance detection accuracy. Various machi

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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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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 (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

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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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Mohammed, Mohammed, and Khattab M. Ali Alheeti. "AI-Driven Features for Intrusion Detection and Prevention Using Random Forest." Journal of Cybersecurity and Information Management 16, no. 1 (2025): 01–14. https://doi.org/10.54216/jcim.160101.

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In this research, we investigate sophisticated methods for Intrusion Detection Systems (IDS) and Intrusion Prevention Systems (IPS), leveraging AI-based feature optimization and diverse machine learning strategies to bolster network intrusion detection and prevention. The study primarily utilizes the NSL-KDD dataset, an enhanced version of the KDD Cup 1999 dataset, chosen for its realistic portrayal of various attack types and for addressing the shortcomings of the original dataset. The methodology includes AI-based feature optimization using Particle Swarm Optimization and Genetic Algorithm,

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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 (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 thro

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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 provi

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Reeja Susan Reji, Anjitha Raj, Riya Raju, Manya, and Sunandha Rajagopal. "Comparative Study of Deep Learning Models for Network Intrusion Detection." international journal of engineering technology and management sciences 7, no. 4 (2023): 313–22. http://dx.doi.org/10.46647/ijetms.2023.v07i04.043.

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In this paper, we present a relative assessment of profound learning ways to deal with network interruption recognition. An Organization Interruption Recognition Framework (NIDS) is a basic part of each and every Web associated framework due to likely goes after from both outer and inside sources. A NIDS is utilized to distinguish network conceived goes after like Forswearing of Administration (DoS) assaults, malware replication, and interlopers that are working inside the framework. Various profound learning approaches have been proposed for interruption identification frameworks. We assess t

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Pinal J. Patel. "IDS Framework based on ML for Cloud Computing." Journal of Information Systems Engineering and Management 10, no. 18s (2025): 278–83. https://doi.org/10.52783/jisem.v10i18s.2913.

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Introduction: The last few decades have seen a marked increase in individuals' dependence on the internet. Users require considerable internet data and a wide variety of services. Cloud computing offers on-demand services under a "pay-as-you-use" framework. Given its open and distributed nature, security is a paramount concern. An intrusion detection system (IDS) serves the purpose of overseeing activities and identifying any unauthorized access or attacks on the computing system. Machine learning (ML) techniques are effective in identifying both known and unknown threats. In this study, we ha

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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

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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 supp

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T. Archana, A. Narmada reddy, A. Datta Sai Kumar reddy, B. Surya Kaushik, and Ms. G Mary Swarna Latha. "Intrusion Detection System Using ANN." International Research Journal on Advanced Engineering Hub (IRJAEH) 2, no. 12 (2024): 2840–46. https://doi.org/10.47392/irjaeh.2024.0393.

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This paper proposes an advanced Intrusion Detection System (IDS) for IoT-based smart cities, utilizing Artificial Neural Networks (ANN) to enhance the detection of network anomalies with a 99% accuracy rate. Compared to CNN and LSTM-based models, this system introduces a multi-classifier capable of identifying five key network attacks: Denial of Service (DoS), User to Root (U2R), Remote to Local (R2L), Probe, and Other attacks. The IDS integrates with a user-friendly web application for real-time anomaly detection, attack type identification, and actionable preventive measures. The proposed mo

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I., A. Hodashinsky, and A. Mech M. "CONSTRUCTING A FUZZY NETWORK INTRUSION CLASSIFIER BASED ON DIFFERENTIAL EVOLUTION AND HARMONIC SEARCH." International Journal of Computer Networks & Communications (IJCNC) 10, no. 2 (2018): 85–91. https://doi.org/10.5281/zenodo.1227885.

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ABSTRACT This paper presents a method for constructing intrusion detection systems based on efficient fuzzy rulebased classifiers. The design process of a fuzzy rule-based classifier from a given input-output data set can be presented as a feature selection and parameter optimization problem. For parameter optimization of fuzzy classifiers, the differential evolution is used, while the binary harmonic search algorithm is used for selection of relevant features. The performance of the designed classifiers is evaluated using the KDD Cup 1999 intrusion detection dataset. The optimal classifier is

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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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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 t

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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 (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 terha

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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 rat

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Guo, Yanmin, Yu Wang, Faheem Khan, et al. "Traffic Management in IoT Backbone Networks Using GNN and MAB with SDN Orchestration." Sensors 23, no. 16 (2023): 7091. http://dx.doi.org/10.3390/s23167091.

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Traffic management is a critical task in software-defined IoT networks (SDN-IoTs) to efficiently manage network resources and ensure Quality of Service (QoS) for end-users. However, traditional traffic management approaches based on queuing theory or static policies may not be effective due to the dynamic and unpredictable nature of network traffic. In this paper, we propose a novel approach that leverages Graph Neural Networks (GNNs) and multi-arm bandit algorithms to dynamically optimize traffic management policies based on real-time network traffic patterns. Specifically, our approach uses

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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 (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. Th

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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 (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 importa

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Putra, Bayu Anugerah, Harun Mukhtar, Elsi Titasari Br Bangun, et al. "OPTIMISASI ALGORITMA K-MEANS DENGAN METODE REDUKSI DIMENSI UNTUK PENGELOMPOKAN BIG DATA DALAM ARSITEKTUR CLOUD COMPUTING." Journal of Software Engineering and Information Systems 5, no. 1 (2021): 1–8. https://doi.org/10.37859/seis.v5i1.7616.

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In the era of big data, data clustering becomes a major challenge due to the complexity and huge volume of data. The K-means algorithm is one of the clustering techniques that is often used due to its simplicity. However, K-means faces difficulties in handling high-dimensional and large-volume data. This study proposes an optimization of the K-means algorithm using the Principal Component Analysis (PCA) dimensionality reduction method to improve the efficiency and accuracy of big data clustering in cloud computing architecture. The KDD Cup 1999 dataset is used to test this method. The dataset

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Shrikant Telang. "Advanced Network Security: An Enhanced BI-LSTM Model for Intelligent Intrusion Detection." Journal of Information Systems Engineering and Management 10, no. 22s (2025): 73–90. https://doi.org/10.52783/jisem.v10i22s.3473.

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Intrusion Detection Systems (IDS) play a crucial role in safeguarding network security against evolving cyber threats. Traditional IDS models often suffer from high false alarm rates and inefficient detection, necessitating the adoption of advanced deep learning techniques. This paper presents an Enhanced Bidirectional Long Short-Term Memory (Enhanced BI-LSTM) model for intrusion detection, integrating Feature Selection, Attention Mechanism, and Regularization to improve accuracy and reduce computational overhead. The proposed model utilizes Principal Component Analysis (PCA) and Chi-Square te

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Paolini, Davide, Pierpaolo Dini, Ettore Soldaini, and Sergio Saponara. "One-Class Anomaly Detection for Industrial Applications: A Comparative Survey and Experimental Study." Computers 14, no. 7 (2025): 281. https://doi.org/10.3390/computers14070281.

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This article aims to evaluate the runtime effectiveness of various one-class classification (OCC) techniques for anomaly detection in an industrial scenario reproduced in a laboratory setting. To address the limitations posed by restricted access to proprietary data, the study explores OCC methods that learn solely from legitimate network traffic, without requiring labeled malicious samples. After analyzing major publicly available datasets, such as KDD Cup 1999 and TON-IoT, as well as the most widely used OCC techniques, a lightweight and modular intrusion detection system (IDS) was developed

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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 (2012): 132–39. http://dx.doi.org/10.20533/ijisr.2042.4639.2012.0016.

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Fang, Menghao, Yixiang Wang, Liangbin Yang, et al. "Reinventing Web Security: An Enhanced Cycle-Consistent Generative Adversarial Network Approach to Intrusion Detection." Electronics 13, no. 9 (2024): 1711. http://dx.doi.org/10.3390/electronics13091711.

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Web3.0, as the link between the physical and digital domains, faces increasing security threats due to its inherent complexity and openness. Traditional intrusion detection systems (IDSs) encounter formidable challenges in grappling with the multidimensional and nonlinear traffic data characteristic of the Web3.0 environment. Such challenges include insufficient samples of attack data, inadequate feature extraction, and resultant inaccuracies in model classification. Moreover, the scarcity of certain traffic data available for analysis by IDSs impedes the system’s capacity to document instance

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Tomi, Yahya Christyawan, Afif Supianto Ahmad, and Firdaus Mahmudy Wayan. "Anomaly-based intrusion detector system using restricted growing self organizing map." Indonesian Journal of Electrical Engineering and Computer Science 13, no. 3 (2019): 919–26. https://doi.org/10.11591/ijeecs.v13.i3.pp919-926.

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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 v

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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, thi

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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 (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 c

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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 (2020): 21–26. http://dx.doi.org/10.5120/ijca2020920661.

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Aliyu. O, Omeiza, Bisallah Hashim. I, Okike Bnjamin, and Sanusi Muhammad. "An Improved Intrusion Detection in Wireless Sensor Networks Using Hybrid Multiclass Over-Sampling and Deep Neural Networks." International Journal of Advances in Scientific Research and Engineering 09, no. 12 (2023): 91–104. http://dx.doi.org/10.31695/ijasre.2023.9.12.8.

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With the emergence of new attacks, there is a continual need for innovative approaches that can closely monitor and swiftly adapt to evolving threats. IDSs can be broadly categorized into misuse detection and anomaly detection, each utilizing machine-learning methods. Machine learning algorithms, particularly those relying on datasets like DARPA and KDD Cup 1999,have gained popularity. However, challenges include dataset limitations, overfitting, and the requirement for substantial computational power. This study focuses on the specific problem of class imbalance in Wireless Sensor Networks (W

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A., M. Chandrashekhar, and Raghuveer K. "FORTIFICATION OF HYBRID INTRUSION DETECTION SYSTEM USING VARIANTS OF NEURAL NETWORKS AND SUPPORT VECTOR MACHINES." International Journal of Network Security & Its Applications (IJNSA) 5, no. 1 (2013): 71–90. https://doi.org/10.5281/zenodo.3873021.

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Intrusion Detection Systems (IDS) form a key part of system defence, where it identifies abnormal activities happening in a computer system. In recent years different soft computing based techniques have been proposed for the development of IDS. On the other hand, intrusion detection is not yet a perfect technology. This has provided an opportunity for data mining to make quite a lot of important contributions in the field of intrusion detection. In this paper we have proposed a new hybrid technique by utilizing data mining techniques such as fuzzy C means clustering, Fuzzy neural network / Ne

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Alzuabidi, Israa Akram. "Building a Resilient Architecture with an Intelligent System Based on Support Vector Machines Algorithm for Cybersecurity." Journal of Electronics,Computer Networking and Applied Mathematics, no. 45 (September 27, 2024): 16–26. http://dx.doi.org/10.55529/jecnam.45.16.26.

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This research focuses on establishing a competent and sustainable cybersecurity structure stimulated by Support Vector Machine (SVM) algorithms based on detection of intrusions. The paper first provides a clear and concise research method that builds on the benchmark dataset known as the KDD Cup 1999 dataset. In particular, with the help of the data collection, preprocessing, and feature selection, the SVM model gives the opportunity to classify different types of the network attack, such as DoS attack or the user-to-root attack. The systematic approach ensures that only the favorable feature

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Zunaida Sitorus, Adittya Pratama, Oky Adinata Hidayatullah, and Adi Widarma. "Model Data Mining Klasifikasi Serangan Siber untuk Deteksi Dini Serangan Menggunakan Algoritma Random Forest." CESS (Journal of Computer Engineering, System and Science) 10, no. 1 (2025): 335–46. https://doi.org/10.24114/cess.v10i1.65940.

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Serangan siber menjadi ancaman yang serius pada era digital saat ini. Deteksi dini serangan sangat penting untuk meminimalkan dampak dari ancaman siber. Dengan identifikasi yang cepat dan akurat, organisasi dapat mengambil langkah-langkah mitigasi yang diperlukan sebelum kerusakan lebih lanjut terjadi. Salah satu pendekatan yang menjanjikan dalam mendeteksi dan mengklasifikasikan serangan siber adalah penggunaan algoritma data mining. Penelitian ini bertujuan untuk mengembangkan model klasifikasi ancaman siber yang lebih komprehensif dengan menggunakan algoritma Random Forest. Random Forest ad

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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 (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

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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 (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

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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-off

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Pratik, Jain, Kholwal Ravikant, and Singh Khurana Tavneet. "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 (IJEAT) 10, no. 4 (2021): 134–43. https://doi.org/10.35940/ijeat.D2413.0410421.

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Abstract:

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 i

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Pratik, Jain, Kholwal Ravikant, and Patidar Muskan. "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 (IJRTE) 10, no. 1 (2021): 110–20. https://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

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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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AmanYadav, Srivastav Abhishek, Tiwari Abhinandan, and Vir Singh Krishna. "Host-based Intrusion Detection System (HIDS)." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 5 (2020): 1043–49. https://doi.org/10.35940/ijeat.E9903.069520.

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This paper presents the data analysis and feature extraction of KDD dataset of 1999. This is used to detect signature based and anomaly attacks on a system. The process is supported by data extraction as well as data cleaning of the above mentioned data set. The dataset consists of 42 parameters and 58 services. These parameters are further filtered to extract useful attributes. Every attack in the dataset is labeled either with “normal” or into four different attack types i.e. denial-of-service, network probe, remote-to-local or user-to-root. Using different machine learning algor

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Arief, Muhammad, Made Gunawan, Agung Septiadi, et al. "A novel framework for analyzing internet of things datasets for machine learning and deep learning-based intrusion detection systems." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 2 (2024): 1574. http://dx.doi.org/10.11591/ijai.v13.i2.pp1574-1584.

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To generate a machine learning (ML) and deep learning (DL) architecture with good performance, we need a decent dataset for the training and testing phases of the development process. Starting with the knowledge discovery and data mining (KDD) Cup 99 dataset, numerous datasets have been produced since 1998 to be utilized in the ML and DL-based intrusion detection systems (IDS) training and testing process. Because there are so many datasets accessible, it might be challenging for researchers to choose which dataset to employ. Therefore, a framework for evaluating dataset appropriateness with t

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Muhammad, Arief, Gunawan Made, Septiadi Agung, et al. "A novel framework for analyzing internet of things datasets for machine learning and deep learning-based intrusion detection systems." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 2 (2024): 1574–84. https://doi.org/10.11591/ijai.v13.i2.pp1574-1584.

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To generate a machine learning (ML) and deep learning (DL) architecture with good performance, we need a decent dataset for the training and testing phases of the development process. Starting with the knowledge discovery and data mining (KDD) Cup 99 dataset, numerous datasets have been produced since 1998 to be utilized in the ML and DL-based intrusion detection systems (IDS) training and testing process. Because there are so many datasets accessible, it might be challenging for researchers to choose which dataset to employ. Therefore, a framework for evaluating dataset appropriateness with t

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Dr., K. Murugan. "Performance Analysis on Anomaly Intrusion Detection in WANET Military Application." IJAPR Journal, UGC Indexed & Care Listed, April 2024 8, no. 1 (2024): 59–67. https://doi.org/10.5281/zenodo.11077162.

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A Wireless ad hoc network is self-configuring and energetic networks in which the nodes are moved in an accidental direction. Due to the mobility of nodes, the various intrusions affect the network performance which causes higher traffic. In order to monitor the traffic in the network, various network intrusion detection systems are located by detecting the malicious node behavior with higher data transmission. WANET is infrastructure-less networks that are mostly used in the considered battlefield, emergency search and rescue operations, as well as civilian ad-hoc circumstances like conferenc

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K., Ramaiah, Sivasankar R., Sreenivasulu G., et al. "Significance of red grape extract on oxidative enzymes in the brain of male albino rat with reference to aging." Biolife 3, no. 2 (2022): 451–60. https://doi.org/10.5281/zenodo.7269502.

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<strong>ABSTRACT</strong> Nicotine has been reported to induce oxidative stress by producing the Reactive Oxygen Species (ROS). In vitro studies showed that Red grape extract has significant antioxidant activity and can inhibit oxidation. Pathogen free, Wistar strain male albino rats were used in the present study, rats were divided into 4 groups of six rats in each group and treated as follows: Group I. Normal Control (NC) (Control rats received 0.9% saline). Group II. Nicotine treated (Nt) (at a dose of 0.6 mg/ kg body weight by subcutaneous injection for a period of 2 months). Group III. Re

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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 (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) (pro

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BéRUBË, BRUNO, LINDA LEFIËVRE, LOUISE COUTU, and ROBERT SULLIVAN. "Regulation of the Epididymal Synthesis of P26h, a Hamster Sperm Protein." Journal of Andrology 17, no. 2 (1996): 104–10. http://dx.doi.org/10.1002/j.1939-4640.1996.tb01758.x.

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ABSTRACT: We have previously identified a 26‐kDa epididymal hamster sperm glycoprotein (P26h) that is involved in gamete interaction. This protein is added to the acrosomal cap during epididymal transit of spermatozoa. Because the epididymis secretes proteins under androgen control, the aim of this study was to document the testicular control of the epididymal ontogenesis of P26h. The cytosolic fraction of the epididymides of male hamsters of different ages, prepared by ultracentrifugation, was used as well as those from mature males at different times following castration. These extracts were

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