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We evaluate methods for applying unsupervised anomaly detection tocybersecurity applications on computer network traffic data, or flow. We borrowfrom the natural language processing literature and conceptualize flow as asort of "language" spoken between machines. Five sequence aggregation rules areevaluated for their efficacy in flagging multiple attack types in a labeledflow dataset, CICIDS2017. For sequence modeling, we rely on long short-termmemory (LSTM) recurrent neural networks (RNN). Additionally, a simplefrequency-based model is described and its performance with respect to attackdetection is compared to the LSTM models. We conclude that the frequency-basedmodel tends to perform as well as or better than the LSTM models for the tasksat hand, with a few notable exceptions.

Sequence Aggregation Rules for Anomaly Detection in Computer Network Traffic