Browsing by Author "Marchany, Randy"

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    Hermes: Boosting the Performance of Machine-Learning-Based Intrusion Detection System through Geometric Feature Learning
    Zhang, Chaoyu; Shi, Shanghao; Wang, Ning; Xu, Xiangxiang; Li, Shaoyu; Zheng, Lizhong; Marchany, Randy; Gardner, Mark; Hou, Y. Thomas; Lou, Wenjing (ACM, 2024-10-14)
    Anomaly-Based Intrusion Detection Systems (IDSs) have been extensively researched for their ability to detect zero-day attacks. These systems establish a baseline of normal behavior using benign traffic data and flag deviations from this norm as potential threats. They generally experience higher false alarm rates than signature-based IDSs. Unlike image data, where the observed features provide immediate utility, raw network traffic necessitates additional processing for effective detection. It is challenging to learn useful patterns directly from raw traffic data or simple traffic statistics (e.g., connection duration, package inter-arrival time) as the complex relationships are difficult to distinguish. Therefore, some feature engineering becomes imperative to extract and transform raw data into new feature representations that can directly improve the detection capability and reduce the false positive rate. We propose a geometric feature learning method to optimize the feature extraction process. We employ contrastive feature learning to learn a feature space where normal traffic instances reside in a compact cluster. We further utilize H-Score feature learning to maximize the compactness of the cluster representing the normal behavior, enhancing the subsequent anomaly detection performance. Our evaluations using the NSL-KDD and N-BaloT datasets demonstrate that the proposed IDS powered by feature learning can consistently outperform state-of-the-art anomaly-based IDS methods by significantly lowering the false positive rate. Furthermore, we deploy the proposed IDS on a Raspberry Pi 4 and demonstrate its applicability on resource-constrained Internet of Things (IoT) devices, highlighting its versatility for diverse application scenarios.
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    WIP: The Feasibility of High-performance Message Authentication in Automotive Ethernet Networks
    Allen, Evan; Bowden, Zeb; Marchany, Randy; Ransbottom, J. Scot (2023-02-27)
    Modern vehicles are increasingly connected systems that expose a wide variety of security risks to their users. Message authentication prevents entire classes of these attacks, such as message spoofing and electronic control unit impersonation, but current in-vehicle networks do not include message authentication features. Latency and throughput requirements for vehicle traffic can be very stringent (<0.1 ms and >100 Mbps in cases), making it difficult to implement message authentication with cryptography due to the overheads required. This work investigates the feasibility of implementing cryptography-based message authentication in Automotive Ethernet networks that is fast enough to comply with these performance requirements. We find that it is infeasible to include Message Authentication Codes in all traffic without costly hardware accelerators and propose alternate approaches for future research.