With the rise in cyber-attacks on computing systems and the rapid increase in use of mobile systems, it is essential to secure these mobile devices. Given that these systems can roam on multiple networks, with no guarantee on security adopted on each network, including Intrusion Detection Systems (IDS) on the mobile platforms can be beneficial in preventing cyber-attacks. One of the key problems with implementing IDS on mobile platforms is the increased power consumption. This paper presents low power circuits that implement the string matching tasks within the Snort IDS. These tasks can take up to 80% of the power consumed for Snort. The circuit presented is based on memristor crossbars and evaluate Snort rules at 0.013mW per signature. The circuits are easy to program, utilizing only two resistance states for the memristors. They are highly parallel and dense, utilizing a brute-force string matching algorithm. These circuits could additionally be utilized for other string matching operations.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Power efficient architecture for network intrusion detection system


    Beteiligte:


    Erscheinungsdatum :

    2014-06-01


    Format / Umfang :

    1532095 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    An Efficient Multi-Task Network for Pedestrian Intrusion Detection

    Shi, Zhenyu / He, Shibo / Sun, Jingchen et al. | IEEE | 2023


    VEHICLE INTRUSION DETECTION SYSTEM AND VEHICLE INTRUSION DETECTION METHOD

    KYUNG TAE HO | Europäisches Patentamt | 2018

    Freier Zugriff

    INTRUSION DETECTION SYSTEM

    OKUYAMA HIROSHI / MATSUI TOSHINORI | Europäisches Patentamt | 2023

    Freier Zugriff

    Vehicle intrusion detection system and vehicle intrusion detection method

    KYUNG TAE HO | Europäisches Patentamt | 2017

    Freier Zugriff

    Feed-forward neural network for Network Intrusion Detection

    Rosay, Arnaud / Carlier, Florent / Leroux, Pascal | IEEE | 2020