Intrusion Detection at Scale with the Assistance of a Command-line Language Model
arxiv(2024)
摘要
Intrusion detection is a long standing and crucial problem in security. A
system capable of detecting intrusions automatically is on great demand in
enterprise security solutions. Existing solutions rely heavily on hand-crafted
rules designed by security operators, which suffer from high false negative
rates and poor generalization ability to new, zero-day attacks at scale. AI and
machine learning offer promising solutions to address the issues, by inspecting
abnormal user behaviors intelligently and automatically from data. However,
existing learning-based intrusion detection systems in the literature are
mostly designed for small data, and they lack the ability to leverage the power
of big data in cloud environments. In this paper, we target at this problem and
introduce an intrusion detection system which incorporates large-scale
pre-training, so as to train a large language model based on tens of millions
of command lines for AI-based intrusion detection. Experiments performed on 30
million training samples and 10 million test samples verify the effectiveness
of our solution.
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