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Recent works within machine learning have been tackling inputs ofever-increasing size, with cybersecurity presenting sequence classificationproblems of particularly extreme lengths. In the case of Windows executablemalware detection, inputs may exceed $100$ MB, which corresponds to a timeseries with $T=100,000,000$ steps. To date, the closest approach to handlingsuch a task is MalConv, a convolutional neural network capable of processing upto $T=2,000,000$ steps. The $\mathcal{O}(T)$ memory of CNNs has preventedfurther application of CNNs to malware. In this work, we develop a new approachto temporal max pooling that makes the required memory invariant to thesequence length $T$. This makes MalConv $116\times$ more memory efficient, andup to $25.8\times$ faster to train on its original dataset, while removing theinput length restrictions to MalConv. We re-invest these gains into improvingthe MalConv architecture by developing a new Global Channel Gating design,giving us an attention mechanism capable of learning feature interactionsacross 100 million time steps in an efficient manner, a capability lacked bythe original MalConv CNN. Our implementation can be found athttps://github.com/NeuromorphicComputationResearchProgram/MalConv2

Classifying Sequences of Extreme Length with Constant Memory Applied to Malware Detection