A decisional framework system for computer network intrusion detection

被引:6
|
作者
Fessi, B. A. [1 ]
Hamdi, M. [1 ]
Benabdallah, S. [1 ]
Boudriga, N. [1 ]
机构
[1] Univ November 7, SUPCOM, CN&S Res Unit, Ecole Super Commun, Carthage, Tunisia
关键词
intrusion detection; incident response; multi-attribute decision theory;
D O I
10.1016/j.ejor.2005.10.020
中图分类号
C93 [管理学];
学科分类号
12 ; 1201 ; 1202 ; 120202 ;
摘要
This paper presents a multi-attribute decisional framework for computer network intrusion detection. First, a cost model that allows to estimate accurately the damage resulting from a security incident is described. Then, a multi-attribute optimization algorithm is applied to select the optimal decision based on alternatives to remedy such incidents. The major interest is that the proposed approach can be applied in collaborative reactive intrusion detection where human experts are assisted by automated tools to find the best response. The approach would allow the possibility to assess the performance of the whole system depending on the performance of each constituents' leading to a definition of optimality conditions on the introduced framework. (c) 2005 Elsevier B.V. All rights reserved.
引用
收藏
页码:1824 / 1838
页数:15
相关论文
共 50 条
  • [21] A statistical Framework for Intrusion Detection System
    Kabir, Md Enamul
    Hu, Jiankun
    2014 11TH INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS AND KNOWLEDGE DISCOVERY (FSKD), 2014, : 941 - 946
  • [22] An Optimized and Hybrid Framework for Image Processing Based Network Intrusion Detection System
    Siddiqi, Murtaza Ahmed
    Pak, Wooguil
    CMC-COMPUTERS MATERIALS & CONTINUA, 2022, 73 (02): : 3921 - 3949
  • [23] Review of AI Techniques in development of Network Intrusion Detection System in SDN Framework
    Dahiya, Seema
    Siwach, Vikas
    Sehrawat, Harkesh
    2021 INTERNATIONAL CONFERENCE ON COMPUTATIONAL PERFORMANCE EVALUATION (COMPE-2021), 2021, : 168 - +
  • [24] Computer Network Intrusion Anomaly Detection with Recurrent Neural Network
    Fu, Zeyuan
    MOBILE INFORMATION SYSTEMS, 2022, 2022
  • [25] An alert data mining framework for network-based intrusion detection system
    Shin, MS
    Jeong, KJ
    INFORMATION SECURITY APPLICATIONS, 2006, 3786 : 38 - 53
  • [26] Framework of Intrusion Detection System via Snort Application on Campus Network Environment
    Ismail, Mohd Nazri
    Ismail, Mohd Taha
    INTERNATIONAL CONFERENCE ON FUTURE COMPUTER AND COMMUNICATIONS, PROCEEDINGS, 2009, : 455 - 459
  • [27] A Centralized Management Framework of Network-based Intrusion Detection and Prevention System
    Wonghirunsombat, Ekgapark
    Asawaniwed, Teewalee
    Hanchana, Vassapon
    Wattanapongsakorn, Naruemon
    Srakaew, Sanan
    Charnsripinyo, Chalermpol
    2013 10TH INTERNATIONAL JOINT CONFERENCE ON COMPUTER SCIENCE AND SOFTWARE ENGINEERING (JCSSE), 2013, : 183 - 188
  • [28] Recurrent network in Network Intrusion Detection System
    Xue, JS
    Sun, JZ
    Zhang, X
    PROCEEDINGS OF THE 2004 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-7, 2004, : 2676 - 2679
  • [29] A sequential deep learning framework for a robust and resilient network intrusion detection system
    Hore, Soumyadeep
    Ghadermazi, Jalal
    Shah, Ankit
    Bastian, Nathaniel D.
    COMPUTERS & SECURITY, 2024, 144
  • [30] The sound of intrusion: A novel network intrusion detection system
    Aldarwbi, Mohammed Y.
    Lashkari, Arash H.
    Ghorbani, Ali A.
    COMPUTERS & ELECTRICAL ENGINEERING, 2022, 104