A supervised topic transition model for detecting malicious system call sequences
作者： Han Xiao , Thomas Stibor
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摘要: We propose a probabilistic model for behavior-based malware detection that jointly models sequential data and class labels. Given labeled sequences (harmless/malicious), our goal is to reveal behavior patterns exploit them predict labels of unknown sequences. The proposed novel extension supervised latent Dirichlet allocation with an estimation algorithm alternates between Gibbs sampling gradient descent. Experiments on real-world set show can learn meaningful patterns, provides competitive performance the task. Moreover, we parallelize training demonstrate scalability varying numbers processors.
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sci-hub.se 参考文章(14)
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