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An overview of anomaly detection techniques: Existing solutions and latest technological trends

Published: 01 August 2007 Publication History

Abstract

As advances in networking technology help to connect the distant corners of the globe and as the Internet continues to expand its influence as a medium for communications and commerce, the threat from spammers, attackers and criminal enterprises has also grown accordingly. It is the prevalence of such threats that has made intrusion detection systems-the cyberspace's equivalent to the burglar alarm-join ranks with firewalls as one of the fundamental technologies for network security. However, today's commercially available intrusion detection systems are predominantly signature-based intrusion detection systems that are designed to detect known attacks by utilizing the signatures of those attacks. Such systems require frequent rule-base updates and signature updates, and are not capable of detecting unknown attacks. In contrast, anomaly detection systems, a subset of intrusion detection systems, model the normal system/network behavior which enables them to be extremely effective in finding and foiling both known as well as unknown or ''zero day'' attacks. While anomaly detection systems are attractive conceptually, a host of technological problems need to be overcome before they can be widely adopted. These problems include: high false alarm rate, failure to scale to gigabit speeds, etc. In this paper, we provide a comprehensive survey of anomaly detection systems and hybrid intrusion detection systems of the recent past and present. We also discuss recent technological trends in anomaly detection and identify open problems and challenges in this area.

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        cover image Computer Networks: The International Journal of Computer and Telecommunications Networking
        Computer Networks: The International Journal of Computer and Telecommunications Networking  Volume 51, Issue 12
        August, 2007
        350 pages

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        Elsevier North-Holland, Inc.

        United States

        Publication History

        Published: 01 August 2007

        Author Tags

        1. Anomaly detection
        2. Data mining
        3. Machine learning
        4. Statistical anomaly detection
        5. Survey

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