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JavaScript Malware Detection Using Locality Sensitive Hashing

Abstract : In this paper, we explore the idea of using locality sensitive hashes as input features to a feed-forward neural network with the goal of detecting JavaScript malware through static analysis. An experiment is conducted using a dataset containing 1.5M evenly distributed benign and malicious samples provided by the anti-malware company Cyren. Four different locality sensitive hashing algorithms are tested and evaluated: Nilsimsa, ssdeep, TLSH, and SDHASH. The results show a high prediction accuracy, as well as low false positive and negative rates. These results show that LSH based neural networks are a competitive option against other state-of-the-art JavaScript malware classification solutions.
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Submitted on : Monday, November 22, 2021 - 3:33:41 PM
Last modification on : Monday, November 22, 2021 - 4:37:40 PM
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Stefan Carl Peiser, Ludwig Friborg, Riccardo Scandariato. JavaScript Malware Detection Using Locality Sensitive Hashing. 35th IFIP International Conference on ICT Systems Security and Privacy Protection (SEC), Sep 2020, Maribor, Slovenia. pp.143-154, ⟨10.1007/978-3-030-58201-2_10⟩. ⟨hal-03440842⟩



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