ADCAS: Adversarial Deep Clustering of Android Streams. (October 2021)

• Record Type: Journal Article
• Authors: Katebi, Matin; Rezakhani, Afshin; Joudaki, Saba
• Is Part Of: Computers & electrical engineering. Volume 95(2021)
• Publication Date: 2021-10
• Subjects: Deep learning -- Android malware stream -- Clustering -- Robustness; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2021.107443
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: The data sequences which are used in malware analysis can be attacked in many applications. However, adversarial attacks are rarely regarded in these types of data. In this paper, a deep learning-based malware clustering approach for sequential data was proposed and the impact of deploying adversarial attacks on it was investigated. An input data stream of android applications was considered as a sequence and the proposed method was tested with the extracted static features of android applications. Three android benchmark datasets, Drebin, Genome, and Contagio, are used to assess the proposed approach. In most experiments, the False Positive Rate (FPR) values of deep clustering algorithms increase to over 60% after the attack, according to the obtained results. Also, the accuracy rates drop to less than 83% in all cases. But by applying the proposed defense method the FPR values reduced while accuracy rates increased.