Anomaly-Based Network Intrusion Detection Using Outlier Subspace Analysis: A Case Study

被引：0
|
作者：
Kershaw, David [1 ]
Gao, Qigang [1 ]
Wang, Hai [2 ]
机构：
[1] Dalhousie Univ, Fac Comp Sci, Halifax, NS B3H 3J5, Canada
[2] St Marys Univ, Sobey Sch Business, San Antonio, TX 78228 USA
来源：
ADVANCES IN ARTIFICIAL INTELLIGENCE | 2011年 / 6657卷
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This paper employs SPOT (Stream Projected Outlier deTector) as a prototype system for anomaly-based intrusion detection and evaluates its performance against other major methods. SPOT is capable of processing high-dimensional data streams and detecting novel attacks which exhibit abnormal behavior, making it a good candidate for network intrusion detection. This paper demonstrates SPOT is effective to distinguish between normal and abnormal processes in a UNIX System Call dataset.
引用
收藏
页码：234 / 239
页数：6
相关论文
共 50 条
  • [21] Anomaly-based intrusion detection using fuzzy rough clustering
    Chimphlee, Witcha
    Abdullah, Abdul Hanan
    Sap, Mohd Noor Md
    Srinoy, Surat
    Chimphlee, Siriporn
    2006 International Conference on Hybrid Information Technology, Vol 1, Proceedings, 2006, : 329 - 334
  • [22] Anomaly-based Intrusion Detection in Computer Networks using Wavelets
    Perlin, Tiago
    Nunes, Raul Ceretta
    Kozakevicius, Alice de Jesus
    REVISTA BRASILEIRA DE COMPUTACAO APLICADA, 2011, 3 (01): : 2 - 15
  • [23] Anomaly-based Intrusion Detection Using Auto-encoder
    Nguimbous, Yves Nsoga
    Ksantini, Riadh
    Bouhoula, Adel
    2019 27TH INTERNATIONAL CONFERENCE ON SOFTWARE, TELECOMMUNICATIONS AND COMPUTER NETWORKS (SOFTCOM), 2019, : 505 - 509
  • [24] Profiling Network Traffic Behavior for the purpose of Anomaly-based Intrusion Detection
    Gill, Manmeet Singh
    Lindskog, Dale
    Zavarsky, Pavol
    2018 17TH IEEE INTERNATIONAL CONFERENCE ON TRUST, SECURITY AND PRIVACY IN COMPUTING AND COMMUNICATIONS (IEEE TRUSTCOM) / 12TH IEEE INTERNATIONAL CONFERENCE ON BIG DATA SCIENCE AND ENGINEERING (IEEE BIGDATASE), 2018, : 885 - 890
  • [25] Anomaly-based Intrusion Detection and Prevention Using Adaptive Boosting in Software-defined Network
    Perwira, Rifki Indra
    Fauziah, Yuli
    Mahendra, I. Putu Retya
    Prasetyo, Dessyanto Boedi
    Simanjuntak, Oliver Samuel
    2019 5TH INTERNATIONAL CONFERENCE ON SCIENCE ININFORMATION TECHNOLOGY (ICSITECH): EMBRACING INDUSTRY 4.0 - TOWARDS INNOVATION IN CYBER PHYSICAL SYSTEM, 2019, : 188 - 192
  • [26] Anomaly-based intrusion detection system using Harris Hawks optimisation with a sigmoid neuron network
    Narengbam, Lenin
    Dey, Shouvik
    INTERNATIONAL JOURNAL OF INFORMATION AND COMPUTER SECURITY, 2024, 24 (1-2) : 5 - 27
  • [27] Anomaly-based network intrusion detection using denoising autoencoder and Wasserstein GAN synthetic attacks
    Arafah, Mohammad
    Phillips, Iain
    Adnane, Asma
    Hadi, Wael
    Alauthman, Mohammad
    Al-Banna, Abedal-Kareem
    APPLIED SOFT COMPUTING, 2025, 168
  • [28] Anomaly-Based Intrusion Detection System Using Support Vector Machine
    Krishnaveni, S.
    Vigneshwar, Palani
    Kishore, S.
    Jothi, B.
    Sivamohan, S.
    ARTIFICIAL INTELLIGENCE AND EVOLUTIONARY COMPUTATIONS IN ENGINEERING SYSTEMS, 2020, 1056 : 723 - 731
  • [29] A brief observation-centric analysis on anomaly-based intrusion detection
    Zhang, ZH
    Shen, H
    INFORMATION SECURITY PRACTICE AND EXPERIENCE, 2005, 3439 : 178 - 191
  • [30] Hybrid Intrusion Detection System using an Unsupervised method for Anomaly-based Detection
    Bhadauria, Saumya
    Mohanty, Tamanna
    2021 IEEE INTERNATIONAL CONFERENCE ON ADVANCED NETWORKS AND TELECOMMUNICATIONS SYSTEMS (IEEE ANTS), 2021,
12345