期刊
NEURAL COMPUTING & APPLICATIONS
卷 34, 期 18, 页码 15165-15174出版社
SPRINGER LONDON LTD
DOI: 10.1007/s00521-021-06755-4
关键词
Android; Malware detection; Fake anti-malware; Neural networks
Android platforms are commonly targeted by attackers, threatening user privacy. Many Android anti-malware applications are fake, necessitating the need for detection. This article presents a dataset and a customized multilayer perceptron neural network for detecting anti-malware.
Android platforms are a popular target for attackers, while many users around the world are victims of Android malwares threatening their private information. Numerous Android anti-malware applications are fake and do not work as advertised because they have been developed either by amateur programmers or by software companies that are not focused on the security aspects of the business. Such applications usually ask for and generally receive non-necessary permissions which at the end collect sensitive information. The rapidly developing fake anti-malware is a serious problem, and there is a need for detection of harmful Android anti-malware. This article delivers a dataset of Android anti-malware, including malicious or benign, and a customized multilayer perceptron neural network that is being used to detect anti-malware based on the permissions of the applications. The results show that the proposed method can detect with very high accuracy fake anti-malware, while it outperforms other standard classifiers in terms of accuracy, precision, and recall.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据