FlowLens: Enabling Efficient Flow Classification for ML-based Network Security Applications

An emerging trend in network security consists in the adoption of programmable switches for performing various security tasks in large-scale, high-speed networks. However, since existing solutions are tailored to specific tasks, they cannot accommodate a growing variety of ML-based security applications, i.e., security-focused tasks that perform targeted flow classification based on packet size or inter-packet frequency distributions with the help of supervised machine learning algorithms. We present FlowLens, a system that leverages programmable switches to efficiently support multi-purpose ML-based security applications. FlowLens collects features of packet distributions at line speed and classifies flows directly on the switches, enabling network operators to re-purpose this measurement primitive at run-time to serve a different flow classification task. To cope with the resource constraints of programmable switches, FlowLens computes for each flow a memory-efficient representation of relevant features, named ``flow marker''. Despite its small size, a flow marker contains enough information to perform accurate flow classification. Since flow markers are highly customizable and application-dependent, FlowLens can automatically parameterize the flow marker generation guided by a multi-objective optimization process that can balance their size and accuracy. We evaluated our system in three usage scenarios: covert channel detection, website fingerprinting, and botnet chatter detection. We find that very small markers enable FlowLens to achieve a 150 fold increase in monitoring capacity for covert channel detection with an accuracy drop of only 3% when compared to collecting full packet distributions.