Study on Classification Algorithms for Network Intrusion Systems 被引量：5

Study on Classification Algorithms for Network Intrusion Systems

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作者 Saravanan Subramanian Vijay Bhanu Srinivasan Chandrasekaran Ramasamy

机构地区 Department of Computer Science&Engineering

出处《通讯和计算机（中英文版）》 2012年第11期1242-1246,共5页 Journal of Communication and Computer

关键词网络攻击分类算法入侵系统入侵检测系统决策树算法信息安全混淆矩阵 IDS Intrusion detection, decision trees, J48, random forest, random tree, NSL-KDD, WEKA.

分类号 TP393.08 [自动化与计算机技术—计算机应用技术] TP311.13 [自动化与计算机技术—计算机科学与技术]

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参考文献13

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10N. Gayatri, S. Nickolas, A.V. Reddy, Feature selection using decision tree induction in class level metrics dataset for software defect predictions, in: Proceedings of the World Congress on Engineering and Computer Science 2010, San Francisco, USA, 2010. 被引量：1

同被引文献1

1Ansam Khraisat,Iqbal Gondal,Peter Vamplew,Joarder Kamruzzaman.Survey of intrusion detection systems:techniques,datasets and challenges[J].Cybersecurity,2019,2(1):1-22. 被引量：29

引证文献5

1Serafeim Moustakidis,Patrik Karlsson.A novel feature extraction methodology using Siamese convolutional neural networks for intrusion detection[J].Cybersecurity,2020,3(1):235-247.
2Ansam Khraisat,Iqbal Gondal,Peter Vamplew,Joarder Kamruzzaman.Survey of intrusion detection systems:techniques,datasets and challenges[J].Cybersecurity,2019,2(1):1-22. 被引量：29
3Ansam Khraisat,Iqbal Gondal,Peter Vamplew,Joarder Kamruzzaman.Survey of intrusion detection systems:techniques,datasets and challenges[J].Cybersecurity,2018,1(1):12-33.
4Serafeim Moustakidis,Patrik Karlsson.A novel feature extraction methodology using Siamese convolutional neural networks for intrusion detection[J].Cybersecurity,2018,1(1):847-859.
5Ansam Khraisat,Ammar Alazab.A critical review of intrusion detection systems in the internet of things:techniques,deployment strategy,validation strategy,attacks,public datasets and challenges[J].Cybersecurity,2021,4(1):251-277. 被引量：4

二级引证文献33

1金志刚,吴桐.基于特征选取与树状Parzen估计的入侵检测[J].系统工程与电子技术,2021,43(7):1954-1960. 被引量：7
2李舒沁.欧盟网络安全战略新动向及其启示[J].网络安全技术与应用,2021(7):175-177. 被引量：1
3张玲翠,许瑶冰,李凤华,房梁,郭云川,李子孚.天地一体化信息网络安全动态赋能架构[J].通信学报,2021,42(9):87-95. 被引量：4
4Ansam Khraisat,Ammar Alazab.A critical review of intrusion detection systems in the internet of things:techniques,deployment strategy,validation strategy,attacks,public datasets and challenges[J].Cybersecurity,2021,4(1):251-277. 被引量：4
5Ke LIU,Mufeng WANG,Rongkuan MA,Zhenyong ZHANG,Qiang WEI.Detection and localization of cyber attacks on water treatment systems:an entropy-based approach[J].Frontiers of Information Technology & Electronic Engineering,2022,23(4):587-603. 被引量：1
6Raisa Abedin Disha,Sajjad Waheed.Performance analysis of machine learning models for intrusion detection system using Gini Impurity-based Weighted Random Forest (GIWRF) feature selection technique[J].Cybersecurity,2022,5(2):119-140. 被引量：5
7Lewis Nkenyereye,Bayu Adhi Tama,Sunghoon Lim.A Stacking-Based Deep Neural Network Approach for Effective Network Anomaly Detection[J].Computers, Materials & Continua,2021(2):2217-2227. 被引量：3
8Shumaila Shahzadi,Fahad Ahmad,Asma Basharat,Madallah Alruwaili,Saad Alanazi,Mamoona Humayun,Muhammad Rizwan,Shahid Naseem.Machine Learning Empowered Security Management and Quality of Service Provision in SDN-NFV Environment[J].Computers, Materials & Continua,2021(3):2723-2749. 被引量：6
9Nahida Islam,Fahiba Farhin,Ishrat Sultana,M.Shamim Kaiser,Md.Sazzadur Rahman,Mufti Mahmud,A.S.M.Sanwar Hosen,Gi Hwan Cho.Towards Machine Learning Based Intrusion Detection in IoT Networks[J].Computers, Materials & Continua,2021(11):1801-1821. 被引量：3
10Magdy M.Fadel,Sally M.El-Ghamrawy,Amr M.T.Ali-Eldin,Mohammed K.Hassan,Ali I.El-Desoky.HDLIDP: A Hybrid Deep Learning Intrusion Detection and Prevention Framework[J].Computers, Materials & Continua,2022(11):2293-2312.

1Tian Xinguang,Duan Miyi,Sun Chunlai,Liu Xin.Detecting network intrusions by data mining and variable-length sequence pattern matching[J].Journal of Systems Engineering and Electronics,2009,20(2):405-411. 被引量：2
2Munehiro Nakamura,Atsushi Otsuka,Haruhiko Kimura.Automatic Selection of Classification Algorithms for Non-Experts Using Meta-Features[J].China-USA Business Review,2014,13(3):199-205.
3Leilei Zhang,Baohui Sheng,Jianli Wang.ONLINE REGULARIZED GENERALIZED GRADIENT CLASSIFICATION ALGORITHMS[J].Analysis in Theory and Applications,2010,26(3):278-300.
4陶智.NIPS的五“指”理论[J].软件世界,2007(19):63-64.
5陶智.网络入侵防御系统的五“指”理论[J].计算机安全,2006(9):46-47.
6陶智.五“指”要素评判NIPS[J].网管员世界,2006(8):119-121.
7邓松,元昌安,赵波,段磊,杨乐婵,饶元,廖剑平.基于GEP和神经网络的属性约简分类算法[J].计算机工程与应用,2006,42(23):154-157. 被引量：4
8卢永菁,王东.基于GPU的高速网络入侵检测系统设计[J].计算机工程与应用,2011,47(33):78-81. 被引量：1
9刘孙俊,王电刚,杨频,张建华.基于免疫多智能体的网络入侵主动防御模型[J].武汉大学学报（信息科学版）,2008,33(10):1055-1058. 被引量：5
10Siva S. Sivatha Sindhu,S. Geetha,M. Marikannan,A. Kannan.A Neuro-genetic Based Short-term Forecasting Framework for Network Intrusion Prediction System[J].International Journal of Automation and computing,2009,6(4):406-414. 被引量：7

通讯和计算机（中英文版）

2012年第11期

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Study on Classification Algorithms for Network Intrusion Systems 被引量：5

参考文献13

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引证文献5

二级引证文献33

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