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On the Limitations of Continual Learning for Malware Classification. (arXiv:2208.06568v1 [cs.CR])
Aug. 16, 2022, 1:20 a.m. | Mohammad Saidur Rahman, Scott E. Coull, Matthew Wright
cs.CR updates on arXiv.org arxiv.org
Malicious software (malware) classification offers a unique challenge for
continual learning (CL) regimes due to the volume of new samples received on a
daily basis and the evolution of malware to exploit new vulnerabilities. On a
typical day, antivirus vendors receive hundreds of thousands of unique pieces
of software, both malicious and benign, and over the course of the lifetime of
a malware classifier, more than a billion samples can easily accumulate. Given
the scale of the problem, sequential training …
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