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Robust Intelligent Malware Detection Using Deep Learning - 2019

Robust Intelligent Malware Detection Using Deep Learning

Research Paper on Robust Intelligent Malware Detection Using Deep Learning

Research Area:  Machine Learning


Security breaches due to attacks by malicious software (malware) continue to escalate posing a major security concern in this digital age. With many computer users, corporations, and governments affected due to an exponential growth in malware attacks, malware detection continues to be a hot research topic. Current malware detection solutions that adopt the static and dynamic analysis of malware signatures and behavior patterns are time consuming and have proven to be ineffective in identifying unknown malwares in real-time. Recent malwares use polymorphic, metamorphic, and other evasive techniques to change the malware behaviors quickly and to generate a large number of new malwares. Such new malwares are predominantly variants of existing malwares, and machine learning algorithms (MLAs) are being employed recently to conduct an effective malware analysis. However, such approaches are time consuming as they require extensive feature engineering, feature learning, and feature representation. By using the advanced MLAs such as deep learning, the feature engineering phase can be completely avoided. Recently reported research studies in this direction show the performance of their algorithms with a biased training data, which limits their practical use in real-time situations. There is a compelling need to mitigate bias and evaluate these methods independently in order to arrive at a new enhanced method for effective zero-day malware detection. To fill the gap in the literature, this paper, first, evaluates the classical MLAs and deep learning architectures for malware detection, classification, and categorization using different public and private datasets. Second, we remove all the dataset bias removed in the experimental analysis by having different splits of the public and private datasets to train and test the model in a disjoint way using different timescales. Third, our major contribution is in proposing a novel image processing technique with optimal parameters.

Robust Intelligent
Malware Detection
Deep Learning
Machine Learning

Author(s) Name:  R. Vinayakumar; Mamoun Alazab; K. P. Soman; Prabaharan Poornachandran; Sitalakshmi Venkatraman Department of Information Technology, Melbourne Polytechnic, Prahran Campus, Melbourne, VIC, Australia

Journal name:  IEEE Access

Conferrence name:  

Publisher name:  IEEE

DOI:  10.1109/ACCESS.2019.2906934

Volume Information:  ( Volume: 7) Page(s): 46717 - 46738