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ADCAS: Adversarial Deep Clustering of Android Streams
Computers & Electrical Engineering ( IF 4.3 ) Pub Date : 2021-09-16 , DOI: 10.1016/j.compeleceng.2021.107443
Matin Katebi 1 , Afshin Rezakhani 1, 2 , Saba Joudaki 3
Affiliation  

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.



中文翻译:

ADCAS:Android Streams 的对抗性深度聚类

恶意软件分析中使用的数据序列可能会在许多应用程序中受到攻击。然而,在这些类型的数据中很少考虑对抗性攻击。在本文中,提出了一种基于深度学习的顺序数据恶意软件聚类方法,并研究了对其部署对抗性攻击的影响。将Android应用程序的输入数据流视为一个序列,并利用提取的Android应用程序静态特征对所提出的方法进行测试。三个 android 基准数据集 Drebin、Genome 和 Contagio 用于评估所提出的方法。在大多数实验中,根据获得的结果,深度聚类算法的误报率(FPR)值在攻击后增加到 60% 以上。此外,在所有情况下,准确率都降至 83% 以下。

更新日期:2021-09-17
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