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    HybrIDS: Embeddable Hybrid Intrusion Detection System

    Lauf, Adrian Peter
    : https://etd.library.vanderbilt.edu/etd-12062007-095827
    http://hdl.handle.net/1803/15166
    : 2007-12-18

    Abstract

    In order to provide preventative security to a homogeneous device network, techniques in addition to static encryption must be implemented to assure network integrity by identifying possible deviant nodes within the collective. This thesis proposes a set of algorithms and techniques for an intrusion detection system, which when combined, provide a two-stage approach that seeks to reduce or eliminate training period requirements, while providing multiple anomaly detection and a degree of self tuning. By utilizing a high level of behavioral abstraction, these intrusion detection techniques can be applied to a broad range of devices, network implementations, and scenarios. Each device node is supplied with an embedded intrusion detection system which allows it to monitor inter-device requests, enabling machine learning techniques for purposes of deviant node analysis. The two principal methods, a maxima detection scheme, and a cross-correlative detection scheme, are combined to create a two-phase detection scheme that can successfully determine deviant node pervasion percentages of up to 22% within the homogeneous device network.
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