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Machine Learning in Cyber-Security - Problems, Challenges and Data Sets
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We present cyber-security problems of high importance. We show that in order to solve these cyber-security problems, one must cope with certain machine learning challenges. We provide novel data sets representing the problems in order to enable the academic community to investigate the problems and suggest methods to cope with the challenges. We also present a method to generate labels via pivoting, providing a solution to common problems of lack of labels in cyber-security.
Forward citations
Cited by 2 Pith papers
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A Comparative Analysis of DNN-based White-Box Explainable AI Methods in Network Security
An evaluation framework for white-box XAI in network intrusion detection reports high robustness, but its own completeness results contradict the claimed advantage.
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On the Veracity of Cyber Intrusion Alerts Synthesized by Generative Adversarial Networks
WGAN-GP with a mutual-information constraint can approximate marginal histograms of per-target NIDS alerts, but the evidence that it improves rare-alert generation is confounded and internally inconsistent.
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