Big-IDS: a decentralized multi agent reinforcement learning approach for distributed intrusion detection in big data networks

被引:2
|
作者
Louati, Faten [1 ]
Ktata, Farah Barika [2 ]
Amous, Ikram [3 ]
机构
[1] Univ Sfax, FSEG Sfax, MIRACL Lab, Sfax, Tunisia
[2] Univ Sousse, MIRACL Lab, ISSATSo, Sousse, Tunisia
[3] Univ Sfax, MIRACL Lab, Enet'com, Sfax, Tunisia
关键词
Intrusion detection system; Multi agent reinforcement learning; Cyber security; Big data; Anomaly detection; Cloud computing; INTERNET; THINGS;
D O I
10.1007/s10586-024-04306-9
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The growing complexity of security threats and the pervasive prevalence of cyberattacks have become more apparent in the present era, and the advent of big data, characterized by its distinctive features, has introduced layers of complexity to security tasks. Intrusion Detection Systems (IDSs) constitute a crucial line of defense, but their adaptation to the realm of big data is imperative. While traditional Machine Learning (ML)-based IDSs have been pivotal in detecting malicious patterns, they are often incapable to keep pace with the demands of expansive big data networks. This paper proposes a novel decentralized Multi-Agent Reinforcement Learning (MARL)-based IDS designed to address the specific challenges posed by big data. Our solution employs decentralized cooperative MARL, securing communicative channels throughout the detection process and concurrent data preprocessing which significantly reduces the overall processing time. Furthermore, the integration of Cloud computing and Big Data streaming techniques further facilitates real-time intrusion detection as cloud's resources allow rapid pre-process and analyse of massive data streams using powerful clusters. Likewise, Big Data streaming techniques ensure that potential intrusions are identified and addressed as they occur. Experimental results, conducted on the widely recognized NSLKDD benchmark dataset, demonstrate the superiority of our solution over other state-of-the-art approaches for big data networks, achieving an accuracy rate of 97.44%.
引用
收藏
页码:6823 / 6841
页数:19
相关论文
共 50 条
  • [1] Empowering Reinforcement Learning on Big Sensed Data for Intrusion Detection
    Otoum, Safa
    Kantarci, Burak
    Mouftah, Hussein
    ICC 2019 - 2019 IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS (ICC), 2019,
  • [2] Big Data-Aware Intrusion Detection System in Communication Networks: a Deep Learning Approach
    Mahdavisharif, Mahzad
    Jamali, Shahram
    Fotohi, Reza
    JOURNAL OF GRID COMPUTING, 2021, 19 (04)
  • [3] Big Data-Aware Intrusion Detection System in Communication Networks: a Deep Learning Approach
    Mahzad Mahdavisharif
    Shahram Jamali
    Reza Fotohi
    Journal of Grid Computing, 2021, 19
  • [4] Intrusion Detection in High-Speed Big Data Networks: A Comprehensive Approach
    Siddique, Kamran
    Akhtar, Zahid
    Kim, Yangwoo
    ADVANCES IN COMPUTER SCIENCE AND UBIQUITOUS COMPUTING, 2018, 474 : 1364 - 1370
  • [5] Multi-agent reinforcement learning for intrusion detection
    Servin, Arturo
    Kudenko, Daniel
    ADAPTIVE AGENTS AND MULTI-AGENT SYSTEMS, 2008, 4865 : 211 - 223
  • [6] A Big Data Analytical Approach to Cloud Intrusion Detection
    Gulmez, Halim Gorkem
    Tuncel, Emrah
    Angin, Pelin
    CLOUD COMPUTING - CLOUD 2018, 2018, 10967 : 377 - 388
  • [7] MAIS-IDS: A distributed intrusion detection system using multi-agent AIS approach
    Seresht, Neda Afzali
    Azmi, Reza
    ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2014, 35 : 286 - 298
  • [8] Distributed deep learning approach for intrusion detection system in industrial control systems based on big data technique and transfer learning
    Abid, Ahlem
    Jemili, Farah
    Korbaa, Ouajdi
    JOURNAL OF INFORMATION AND TELECOMMUNICATION, 2023, 7 (04) : 513 - 541
  • [9] Machine Learning Intrusion Detection in Big Data Era: A Multi-Objective Approach for Longer Model Lifespans
    Viegas, Eduardo
    Santin, Altair Olivo
    Abreu Jr, Vilmar
    IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING, 2021, 8 (01): : 366 - 376
  • [10] A Distributed Intelligent Intrusion Detection System based on Parallel Machine Learning and Big Data Analysis
    Louati, Faten
    Ktata, Farah Barika
    Ben Amor, Ikram Amous
    PROCEEDINGS OF THE 11TH INTERNATIONAL CONFERENCE ON SENSOR NETWORKS (SENSORNETS), 2021, : 152 - 157