An ensemble framework for interpretable malicious code detection. Issue 12 (13th October 2020)

• Record Type: Journal Article
• Authors: Cheng, Jieren; Zheng, Jiachen; Yu, Xiaomei
• Is Part Of: International journal of intelligent systems. Volume 37:Issue 12(2022)
• Page Start: 10100
• Page End: 10117
• Publication Date: 2020-10-13
• Subjects: feature extraction -- knowledge graph -- machine learning -- malicious code -- malware detection; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; 006.3
• DOI: 10.1002/int.22310
• Languages: English
• ISSNs: 0884-8173

Abstract

Abstract: Malicious code is an ever‐growing security threats to computer systems and networks, while malware detection provides effective defense against malicious codes. In this paper, a brief overview is presented on currently prevalent methods to detect malicious codes, including signature‐based methods, behavioral‐based detection and machine learning (ML) based ones. More specifically, the potentially effective malicious features are summarized and the novel methods using ML are deeply discussed. Furthermore, an ensemble interpretable framework is explored for automatic and efficient malicious code detection. Based on the knowledge graph of malware, the novel framework inclines to achieve robust malware detection even confronted with unseen malicious codes. Finally, both advantages and disadvantages are discussed and experimental results are outlined to verify the effectiveness of the novel methods.