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Machine Learning (ML) techniques are increasingly adopted to tackleever-evolving high-profile network attacks, including DDoS, botnet, andransomware, due to their unique ability to extract complex patterns hidden indata streams. These approaches are however routinely validated with datacollected in the same environment, and their performance degrades when deployedin different network topologies and/or applied on previously unseen traffic, aswe uncover. This suggests malicious/benign behaviors are largely learnedsuperficially and ML-based Network Intrusion Detection System (NIDS) needrevisiting, to be effective in practice. In this paper we dive into themechanics of large-scale network attacks, with a view to understanding how touse ML for Network Intrusion Detection (NID) in a principled way. We revealthat, although cyberattacks vary significantly in terms of payloads, vectorsand targets, their early stages, which are critical to successful attackoutcomes, share many similarities and exhibit important temporal correlations.Therefore, we treat NID as a time-sensitive task and propose NetSentry, perhapsthe first of its kind NIDS that builds on Bidirectional Asymmetric LSTM(Bi-ALSTM), an original ensemble of sequential neural models, to detect networkthreats before they spread. We cross-evaluate NetSentry using two practicaldatasets, training on one and testing on the other, and demonstrate F1 scoregains above 33% over the state-of-the-art, as well as up to 3 times higherrates of detecting attacks such as XSS and web bruteforce. Further, we putforward a novel data augmentation technique that boosts the generalizationabilities of a broad range of supervised deep learning algorithms, leading toaverage F1 score gains above 35%.

NetSentry: A Deep Learning Approach to Detecting Incipient Large-scale Network Attacks