An Efficient One-Class SVM for Novelty Detection in IoT
An Efficient One-Class SVM for Novelty Detection in IoT
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发表时间：
2022
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通讯作者：
Kun Yang;Samory Kpotufe;N. Feamster
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作者：
Kun Yang;Samory Kpotufe;N. Feamster
单级支持矢量机（OCSVM）是一组新颖性检测的方法，因为它们在正常数据和新颖数据之间拟合复杂的非线性边界的灵活性在物联网（“ IoT”）之间很重要（“ IoT”）。物联网设备可以呈现的潜在威胁，并且由于IoT设备的各种设备，交通模式和异常，OCSVM通常在这些环境中表现良好。不幸的是，传统的OCSVM可以在检测中引入禁止的记忆和计算开销，并评估有效的OCSVM在预测时间内实现15-30倍加速的模型，而记忆要求减少30-40倍，而无需牺牲检测准确性。
One-Class Support Vector Machines (OCSVMs) are a set of common approaches for novelty detection due to their flexibility in fitting complex nonlinear boundaries between normal and novel data. Novelty detection is important in the Internet of Things (“IoT”) due to the potential threats that IoT devices can present, and OCSVMs often perform well in these environments due to the variety of devices, traffic patterns, and anomalies that IoT devices present. Unfortunately, conventional OCSVMs can introduce prohibitive memory and computational overhead in detection. This work designs, implements, and evaluates an efficient OCSVM for such practical settings. We extend Nyström and (Gaussian) Sketching approaches to OCSVM, combining these methods with clustering and Gaussian mixture models to achieve 15-30x speedup in prediction time and 30-40x reduction in memory requirements without sacrificing detection accuracy. Here, the very nature of IoT devices is crucial: they tend to admit few modes of normal operation, allowing for efficient pattern compression.