Tao Yi1,3, Xingshu Chen1,2,*, Mingdong Yang3, Qindong Li1, Yi Zhu1
CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 929-955, 2024, DOI:10.32604/cmes.2024.048793
Abstract Due to the rapid evolution of Advanced Persistent Threats (APTs) attacks, the emergence of new and rare attack samples, and even those never seen before, make it challenging for traditional rule-based detection methods to extract universal rules for effective detection. With the progress in techniques such as transfer learning and meta-learning, few-shot network attack detection has progressed. However, challenges in few-shot network attack detection arise from the inability of time sequence flow features to adapt to the fixed length input requirement of deep learning, difficulties in capturing rich information from original flow in the case of insufficient samples, and the… More >
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