Malware Detection and Classification Based on N-Grams Attribute Similarity
Zhang Fuyong, Zhao Tiezhu · 2017
Unknown malware has increased dramatically, but the existing security software cannot identify them effectively. In this paper, we propose a new malware detection and classification method based on n-grams attribute similarity. We extract all n-grams of byte codes from training samples and select the most relevant as attributes. After calculating the average value of attributes in malware and benign separately, we determine a test sample is malware or benign by attribute similarity between attributes of the test sample and the two average attributes of malware and benign. We compare our method with a variety of machine learning methods, including Naïve Bayes, Bayesian Networks, Support Vector Machine and C4.5 Decision Tree. Experimental results on public (Open Malware Benchmark) and private (self-collected) datasets both reveal that our method outperforms the other four methods.