首页|Malware classification using gray-scale images and ensemble learning
Malware classification using gray-scale images and ensemble learning
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NETL
Malware authors can easily generate obfuscated and metamorphic malware to evade the detection of anti-virus software using automated toolkits。 To identify these variations， we design an efficient system to detect and classify the malicious software。 We construct a controlled disassembly files from malware。 And we convert disassembly files into gray-scale images。 In order to improve the efficiency， we use the local mean method to compress gray-scale images， which are mapped into feature vectors。 To classify malware， we propose a novel ensemble learning which is based on K-means and the diversity selection。 Finally， our experiments show that our method is able to effectively classify the malware。
NETL
