Analysis of Machine Learning Techniques Based Intrusion Detection Systems

被引:14
|
作者
Sharma, Rupam Kr. [1 ]
Kalita, Hemanta Kumar [1 ]
Borah, Parashjyoti [2 ]
机构
[1] North Eastern Hills Univ, Shillong, Meghalaya, India
[2] Assam Don Bosco Univ, Gauhati, India
关键词
Intrusion detection system; Supervised learning; Unsupervised learning; KDD'99; Anomaly detection; Host intrusion system;
D O I
10.1007/978-81-322-2529-4_51
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Attacks on Computer Networks are one of the major threats on using Internet these days. Intrusion Detection Systems (IDS) are one of the security tools available to detect possible intrusions in a Network or in a Host. Research showed that application of machine learning techniques in intrusion detection could achieve high detection rate as well as low false positive rate. This paper discusses some commonly used machine learning techniques in Intrusion Detection System and also reviews some of the existing machine learning IDS proposed by authors at different times.
引用
收藏
页码:485 / 493
页数:9
相关论文
共 50 条
  • [41] Impact of Features Reduction on Machine Learning Based Intrusion Detection Systems
    Fatima, Masooma
    Rehman, Osama
    Rahman, Ibrahim M. H.
    EAI ENDORSED TRANSACTIONS ON SCALABLE INFORMATION SYSTEMS, 2022, 9 (06)
  • [42] Machine learning-based intrusion detection for SCADA systems in healthcare
    Öztürk, Tolgahan
    Turgut, Zeynep
    Akgün, Gökçe
    Köse, Cemal
    Network Modeling Analysis in Health Informatics and Bioinformatics, 2022, 11 (01)
  • [43] Machine learning-based intrusion detection for SCADA systems in healthcare
    Tolgahan Öztürk
    Zeynep Turgut
    Gökçe Akgün
    Cemal Köse
    Network Modeling Analysis in Health Informatics and Bioinformatics, 2022, 11
  • [44] A survey of intrusion detection from the perspective of intrusion datasets and machine learning techniques
    Singh G.
    Khare N.
    International Journal of Computers and Applications, 2022, 44 (07) : 659 - 669
  • [45] Design of advanced intrusion detection systems based on hybrid machine learning techniques in hierarchically wireless sensor networks
    Gebremariam, Gebrekiros Gebreyesus
    Panda, J.
    Indu, S.
    CONNECTION SCIENCE, 2023, 35 (01)
  • [46] Intrusion detection systems for IoT based on bio-inspired and machine learning techniques: a systematic review of the literature
    Saadouni, Rafika
    Gherbi, Chirihane
    Aliouat, Zibouda
    Harbi, Yasmine
    Khacha, Amina
    CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS, 2024, 27 (07): : 8655 - 8681
  • [47] Intrusion detection systems for software-defined networks: a comprehensive study on machine learning-based techniques
    Mustafa, Zaid
    Amin, Rashid
    Aldabbas, Hamza
    Ahmed, Naeem
    CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS, 2024, 27 (07): : 9635 - 9661
  • [48] Intrusion detection based on phishing detection with machine learning
    Jayaraj R.
    Pushpalatha A.
    Sangeetha K.
    Kamaleshwar T.
    Udhaya Shree S.
    Damodaran D.
    Measurement: Sensors, 2024, 31
  • [49] Intrusion detection method based on machine learning
    Tian, Xin-Guang
    Gao, Li-Zhi
    Zhang, Er-Yang
    Tongxin Xuebao/Journal on Communications, 2006, 27 (06): : 108 - 114
  • [50] Machine Learning Based Network Intrusion Detection
    Lee, Chie-Hong
    Su, Yann-Yean
    Lin, Yu-Chun
    Lee, Shie-Jue
    2017 2ND IEEE INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE AND APPLICATIONS (ICCIA), 2017, : 79 - 83