Enhanced relevant feature selection model for intrusion detection systems
作者： Ayman I. Madbouly , Tamer M. Barakat
DOI: 10.1504/IJIEI.2016.074499
关键词: Machine learning 、 Statistical classification 、 Artificial intelligence 、 Network security 、 Supervised learning 、 Constant false alarm rate 、 Intrusion detection system 、 Data mining 、 Feature selection 、 Measure (data warehouse) 、 Anomaly-based intrusion detection system 、 Computer science
摘要: With the increased amount of network threats and intrusions, finding an efficient reliable defence measure has a great focus as research field. Intrusion detection systems IDSs have been widely deployed effective for existing networks. detect anomalies based on features extracted from traffic. Network traffic many to measure. The problem is that with huge we can irrelevant features. These usually affect performance rate consume resources. In this paper, proposed enhanced model increase attacks accuracy improve overall system performance. We measured verified its effectiveness feasibility by comparing it nine-different models used 41-features dataset. results showed that, our could efficiently achieves high rate, low false alarm fast process.
sci-hub.se 参考文章(37)
Heba F Eid, Ahmad Taher Azar, Aboul Ella Hassanien, None, Improved Real-Time Discretize Network Intrusion Detection System BIC-TA (1). pp. 99- 109 ,(2013) , 10.1007/978-81-322-1038-2_9
Jin Xu, Guang Yang, Hong Man, Haibo He, L 1 graph based on sparse coding for feature selection international symposium on neural networks. pp. 594- 601 ,(2013) , 10.1007/978-3-642-39065-4_71
Shailendra Singh, Sanjay Silakari, An ensemble approach for feature selection of Cyber Attack Dataset arXiv: Cryptography and Security. ,(2009)
Stefan Axelsson, Intrusion Detection Systems: A Survey and Taxonomy ,(2002)
Amira Sayed A. Aziz, Ahmad Taher Azar, Aboul Ella Hassanien, Sanaa El-Ola Hanafy, Continuous Features Discretization for Anomaly Intrusion Detectors Generation Advances in Intelligent Systems and Computing. pp. 209- 221 ,(2014) , 10.1007/978-3-319-00930-8_19
Wenying Feng, Qinglei Zhang, Gongzhu Hu, Jimmy Xiangji Huang, Mining network data for intrusion detection through combining SVMs with ant colony networks Future Generation Computer Systems. ,vol. 37, pp. 127- 140 ,(2014) , 10.1016/J.FUTURE.2013.06.027
Zulaiha Ali Othman, Azuraliza Abu Bakar, Intesar Etubal, Improving signature detection classification model using features selection based on customized features intelligent systems design and applications. pp. 1026- 1031 ,(2010) , 10.1109/ISDA.2010.5687051
Wei-Chao Lin, Shih-Wen Ke, Chih-Fong Tsai, CANN: An intrusion detection system based on combining cluster centers and nearest neighbors Knowledge Based Systems. ,vol. 78, pp. 13- 21 ,(2015) , 10.1016/J.KNOSYS.2015.01.009
Yinhui Li, Jingbo Xia, Silan Zhang, Jiakai Yan, Xiaochuan Ai, Kuobin Dai, An efficient intrusion detection system based on support vector machines and gradually feature removal method Expert Systems With Applications. ,vol. 39, pp. 424- 430 ,(2012) , 10.1016/J.ESWA.2011.07.032
Basant Agarwal, Namita Mittal, Hybrid Approach for Detection of Anomaly Network Traffic using Data Mining Techniques Procedia Technology. ,vol. 6, pp. 996- 1003 ,(2012) , 10.1016/J.PROTCY.2012.10.121