Yazar "Mohamed, Tamara Saad" seçeneğine göre listele

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    IoT-Based Intrusion Detection Systems: A Review
    (Taylor & Francis Ltd, 2022) Mohamed, Tamara Saad; Aydin, Sezgin
    Internet of Things (IoT) is a modern prototype that merges physical entities affiliated with a variety of fields, like industrial tasks, firm automation, mortal fitness, and habitat observance with the internet. It intensifies the occupancy of Internet-linked objects in everyday tasks, resulting in issues linked to security besides usefulness. For many years, Intrusion Detection Systems (IDS) have proven to be advantageous for guarding information systems and networks. Conversely, enacting old IDS procedures on IoT is unrealistic owing to some specific elements. For instance, specific protocol stacks, strained-asset gadgets, and measures. This study furnishes inspection of IDS inquiry achievements for IoT. The objective entails the establishment of key biases, general drawbacks, and inquiry guidelines. Grouping of the suggested IDSs was undertaken in the state-of-art regarding the subsequent qualities: discernment technique, IDS placement procedure, security peril, and confirmation procedure. The paper further considered every attribute shortcoming, exploring views of material with suggestions on certain IDS procedures for IoT or inaugurating attack distinguishing procedures for IoT menaces with the possibility of being fixed in IDSs. [GRAPHICS] .
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    Kalman and Cauchy clustering for anomaly detection based authentication of IoMTs using extreme learning machine
    (Inst Engineering Technology-Iet, 2022) Mohamed, Tamara Saad; Aydin, Sezgin; Alkhayyat, Ahmed; Malik, Rami Q.
    The vulnerabilities of the Internet of Things (IoTs) in general and the Internet of Mobile Things (IoMTs) in particular motivate researchers to equip them with security systems against intruders and attacks. The integration of anomaly detection with intrusion detection for IoMTs has not been addressed adequately. This paper tackles this issue through building a Kalman filter and Cauchy clustering algorithm for anomaly detection and using them for authentication nodes within IoMTs using the Extreme Learning Machine classifier. The algorithm of this proposed work is composed of various components; first, the Kalman filter-based model for estimating the trajectory of pedestrians within an indoor environment based on fusing WiFi with IMU data. Second, trustworthiness assessment for detecting anomaly behaviour in IoMT based on the estimated trajectory using the Kalman filter. Third, the trust IDS model for IoMT systems by integrating anomaly detection with online learning for attacks identification using an online sequential extreme learning machine. The algorithm has been implemented and evaluated using TamperU dataset for WiFi fingerprinting and KDD99 for intrusion detection. Furthermore, a comparison with benchmarks (the algorithms which used in other studies) for intrusion and anomaly detection proves the superiority of this proposed approach in terms of all the considered classification metrics.