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
  • [31] Panacea: Automating Attack Classification for Anomaly-Based Network Intrusion Detection Systems
    Bolzoni, Damiano
    Etalle, Sandro
    Hartel, Pieter H.
    RECENT ADVANCES IN INTRUSION DETECTION, PROCEEDINGS, 2009, 5758 : 1 - 20
  • [32] Insights into Anomaly-based Intrusion Detection Systems Usability. A Case Study using Real HTTP Requests
    Diaz-Verdejo, Jesus E.
    Alonso, Rafael Estepa
    Alonso, Antonio Estepa
    Munoz-Calle, Javier
    PROCEEDINGS OF THE 2024 EUROPEAN INTERDISCIPLINARY CYBERSECURITY CONFERENCE, EICC 2024, 2024, : 82 - 89
  • [33] POSEIDON: a 2-tier anomaly-based network intrusion detection system
    Bolzoni, Damiano
    Etalle, Sandro
    Hartel, Pieter
    Zambon, Emmanuele
    FOURTH IEEE INTERNATIONAL WORKSHOP ON INFORMATION ASSURANCE, PROCEEDINGS, 2006, : 144 - +
  • [34] Building Multiclass Classification Baselines for Anomaly-based Network Intrusion Detection Systems
    Shah, Ajay
    Clachar, Sophine
    Minimair, Manfred
    Cook, Davis
    2020 IEEE 7TH INTERNATIONAL CONFERENCE ON DATA SCIENCE AND ADVANCED ANALYTICS (DSAA 2020), 2020, : 759 - 760
  • [35] Anomaly-Based Intrusion Detection Using Machine Learning: An Ensemble Approach
    Lalduhsaka R.
    Bora N.
    Khan A.K.
    International Journal of Information Security and Privacy, 2022, 16 (01):
  • [36] Anomaly-Based Intrusion Detection in IIoT Networks Using Transformer Models
    Casajus-Setien, Jorge
    Bielza, Concha
    Larranaga, Pedro
    2023 IEEE INTERNATIONAL CONFERENCE ON CYBER SECURITY AND RESILIENCE, CSR, 2023, : 72 - 77
  • [37] Anomaly-based Intrusion Detection using Tree Augmented Naive Bayes
    Wester, Philip
    Heiding, Fredrik
    Lagerstrom, Robert
    2021 IEEE 25TH INTERNATIONAL ENTERPRISE DISTRIBUTED OBJECT COMPUTING CONFERENCE WORKSHOPS (EDOCW 2021), 2021, : 112 - 121
  • [38] An Anomaly-based Intrusion Detection System Using Butterfly Optimization Algorithm
    Mahboob, Amir Soltany
    Moghaddam, Mohammad Reza Ostadi
    2020 6TH IRANIAN CONFERENCE ON SIGNAL PROCESSING AND INTELLIGENT SYSTEMS (ICSPIS), 2020,
  • [39] Undermining an anomaly-based intrusion detection system using common exploits
    Tan, KMC
    Killourhy, KS
    Maxion, RA
    RECENT ADVANCES IN INTRUSION DETECTION, PROCEEDINGS, 2002, 2516 : 54 - 73
  • [40] Constant False Alarm Rate Anomaly-Based Approach for Network Intrusion Detection
    AlShaalan, Rayan
    AsSadhan, Basil
    Al-Muhtadi, Jalal
    Bin-Abbas, Hesham
    Abd El-Samie, Fathi
    Alshebeili, Saleh
    2013 10TH INTERNATIONAL CONFERENCE ON HIGH CAPACITY OPTICAL NETWORKS AND ENABLING TECHNOLOGIES (HONET-CNS), 2013, : 141 - 145
12345