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      MDEA: Malware Detection with Evolutionary Adversarial Learning

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          Abstract

          Malware detection have used machine learning to detect malware in programs. These applications take in raw or processed binary data to neural network models to classify as benign or malicious files. Even though this approach has proven effective against dynamic changes, such as encrypting, obfuscating and packing techniques, it is vulnerable to specific evasion attacks where that small changes in the input data cause misclassification at test time. This paper proposes a new approach: MDEA, an Adversarial Malware Detection model uses evolutionary optimization to create attack samples to make the network robust against evasion attacks. By retraining the model with the evolved malware samples, its performance improves a significant margin.

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          Author and article information

          Journal
          09 February 2020
          Article
          2002.03331
          4b911de8-0461-4010-9c8a-94c50a36ab67

          http://arxiv.org/licenses/nonexclusive-distrib/1.0/

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          Custom metadata
          8 pages, 7 figures
          cs.CR cs.NE

          Security & Cryptology,Neural & Evolutionary computing
          Security & Cryptology, Neural & Evolutionary computing

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