A Secure Encryption-Based Malware Detection System

• Zhaowen Lin,
• Fei Xiao,
• Yi Sun,
• Yan Ma,
• Cong-Cong Xing,
• Jun Huang,

Vol. 12, No. 4, April 29, 2018
10.3837/tiis.2018.04.022, Download Paper (Free):

• Malware detection
• detection mechanism
• API call fragments
• homomorphic encryption
• privacy-preserving Naive Bayes classifier

Abstract

Malware detections continue to be a challenging task as attackers may be aware of the rules used in malware detection mechanisms and constantly generate new breeds of malware to evade the current malware detection mechanisms. Consequently, novel and innovated malware detection techniques need to be investigated to deal with this circumstance. In this paper, we propose a new secure malware detection system in which API call fragments are used to recognize potential malware instances, and these API call fragments together with the homomorphic encryption technique are used to construct a privacy-preserving Naive Bayes classifier (PP-NBC). Experimental results demonstrate that the proposed PP-NBC can successfully classify instances of malware with a hit-rate as high as 94.93%.

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