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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.