Feature Subset Selection Using Genetic Algorithm for Intrusion Detection System

被引：1
|
作者：
Behjat, Amir Rajabi [1 ]
Vatankhah, Najmeh [1 ]
Mustapha, Aida [1 ]
机构：
[1] Univ Putra Malaysia, Fac Comp Sci & Informat Technol, Upm Serdang 43400, Selangor, Malaysia
来源：
ADVANCED SCIENCE LETTERS | 2014年 / 20卷 / 01期
关键词：
Intrusion Detection System; KDD; 99; Dataset; Genetic Algorithm; Feature Selection;
D O I：
10.1166/asl.2014.5270
中图分类号：
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号：
07 ; 0710 ; 09 ;
摘要：
KDD 99 intrusion detection datasets, which are based on DARPA 98, is a labeled dataset studied in the field of intrusion detection. The respected KDD dataset contains numerous features, in which some of them are irrelevant or less effective to detect the attacks. This study is set to improve the classification of intrusions by means of selecting significant features. Binary Genetic Algorithm (BGA) is proposed for feature selection in order to decrease the number of unrelated features. The selected features are then become the input for the classification task using a standard Multi-layer Perceptron (MLP) classifier. The results achieved show very high classification accuracy and low false positive rate with the lowest CPU time.
引用
收藏
页码：235 / 238
页数：4
相关论文
共 50 条
  • [1] Modified genetic algorithm based feature subset selection in intrusion detection system
    Zhu, YX
    Shan, X
    Guo, J
    INTERNATIONAL SYMPOSIUM ON COMMUNICATIONS AND INFORMATION TECHNOLOGIES 2005, VOLS 1 AND 2, PROCEEDINGS, 2005, : 9 - 12
  • [2] Feature Subset Selection Using Binary Gravitational Search Algorithm for Intrusion Detection System
    Behjat, Amir Rajabi
    Mustapha, Aida
    Nezamabadi-pour, Hossein
    Sulaiman, Md. Nasir
    Mustapha, Norwati
    INTELLIGENT INFORMATION AND DATABASE SYSTEMS (ACIIDS 2013), PT II, 2013, 7803 : 377 - 386
  • [3] Feature selection using a genetic algorithm for intrusion detection
    Helmer, G
    Wong, J
    Honavar, V
    Miller, L
    GECCO-99: PROCEEDINGS OF THE GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE, 1999, : 1781 - 1781
  • [4] Intrusion Detection System using Bayesian Network and Feature Subset Selection
    Jabbar, M. A.
    Aluvalu, Rajanikanth
    Reddy, S. Sai Satyanarayana
    2017 IEEE INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE AND COMPUTING RESEARCH (ICCIC), 2017, : 640 - 644
  • [5] Feature Selection Using Genetic Algorithm to Improve Classification in Network Intrusion Detection System
    Ferriyan, Andrey
    Thamrin, Achmad Husni
    Takeda, Keiji
    Murai, Jun
    2017 INTERNATIONAL ELECTRONICS SYMPOSIUM ON KNOWLEDGE CREATION AND INTELLIGENT COMPUTING (IES-KCIC), 2017, : 46 - 49
  • [6] Feature subset selection using a genetic algorithm
    Yang, JH
    Honavar, V
    IEEE INTELLIGENT SYSTEMS & THEIR APPLICATIONS, 1998, 13 (02): : 44 - 49
  • [7] Improving Classification Accuracy of Intrusion Detection System using Feature Subset Selection
    Bahl, Shilpa
    Sharma, Sudhir Kumar
    2015 5TH INTERNATIONAL CONFERENCE ON ADVANCED COMPUTING & COMMUNICATION TECHNOLOGIES ACCT 2015, 2015, : 431 - 436
  • [8] Feature Subset Selection for Network Intrusion Detection Mechanism Using Genetic Eigen Vectors
    Ahmad, Iftikhar
    Abdulah, Azween B.
    Alghamdi, Abdullah S.
    AInfajan, Khaled
    Hussain, Muhammad
    COMPUTER COMMUNICATION AND MANAGEMENT, 2011, 5 : 75 - 79
  • [9] Genetic Feature Selection in Intrusion Detection System
    Han, Myung-Mook
    Kim, Jaehyoun
    Jeong, Taikyeong
    INFORMATION-AN INTERNATIONAL INTERDISCIPLINARY JOURNAL, 2011, 14 (02): : 493 - 502
  • [10] Optimization of Network Intrusion Detection System Using Genetic Algorithm with Improved Feature Selection Technique
    Matel, Elmer C.
    Sison, Arid M.
    Medina, Ruji P.
    2019 IEEE 11TH INTERNATIONAL CONFERENCE ON HUMANOID, NANOTECHNOLOGY, INFORMATION TECHNOLOGY, COMMUNICATION AND CONTROL, ENVIRONMENT, AND MANAGEMENT (HNICEM), 2019,
12345