MVDroid: an android malicious VPN detector using neural networks.

IF 4.5 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Saeed Seraj, Siavash Khodambashi, Michalis Pavlidis, Nikolaos Polatidis
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引用次数: 0

Abstract

The majority of Virtual Private Networks (VPNs) fail when it comes to protecting our privacy. If we are using a VPN to protect our online privacy, many of the well-known VPNs are not secure to use. When examined closely, VPNs can appear to be perfect on the surface but still be a complete privacy and security disaster. Some VPNs will steal our bandwidth, infect our computers with malware, install secret tracking libraries on our devices, steal our personal data, and leave our data exposed to third parties. Generally, Android users should be cautious when installing any VPN software on their devices. As a result, it is important to identify malicious VPNs before downloading and installing them on our Android devices. This paper provides an optimised deep learning neural network for identifying fake VPNs, and VPNs infected by malware based on the permissions of the apps, as well as a novel dataset of malicious and benign Android VPNs. Experimental results indicate that our proposed classifier identifies malicious VPNs with high accuracy, while it outperforms other standard classifiers in terms of evaluation metrics such as accuracy, precision, and recall.

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MVDroid:一个使用神经网络的安卓恶意VPN检测器。
当涉及到保护我们的隐私时,大多数虚拟专用网络(VPN)都会失败。如果我们使用VPN来保护我们的在线隐私,那么许多知名的VPN使用起来并不安全。当仔细检查时,VPN表面上看起来很完美,但仍然是一场完全的隐私和安全灾难。一些VPN会窃取我们的带宽,用恶意软件感染我们的计算机,在我们的设备上安装秘密跟踪库,窃取我们的个人数据,并将我们的数据暴露给第三方。一般来说,安卓用户在设备上安装任何VPN软件时都应谨慎。因此,在我们的Android设备上下载和安装恶意VPN之前,识别它们是很重要的。本文提供了一个优化的深度学习神经网络,用于根据应用程序的权限识别假VPN和被恶意软件感染的VPN,以及一个新的恶意和良性Android VPN数据集。实验结果表明,我们提出的分类器识别恶意VPN的准确率很高,而在准确性、准确度和召回率等评估指标方面,它优于其他标准分类器。
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来源期刊
Neural Computing & Applications
Neural Computing & Applications 工程技术-计算机:人工智能
CiteScore
11.40
自引率
8.30%
发文量
1280
审稿时长
6.9 months
期刊介绍: Neural Computing & Applications is an international journal which publishes original research and other information in the field of practical applications of neural computing and related techniques such as genetic algorithms, fuzzy logic and neuro-fuzzy systems. All items relevant to building practical systems are within its scope, including but not limited to: -adaptive computing- algorithms- applicable neural networks theory- applied statistics- architectures- artificial intelligence- benchmarks- case histories of innovative applications- fuzzy logic- genetic algorithms- hardware implementations- hybrid intelligent systems- intelligent agents- intelligent control systems- intelligent diagnostics- intelligent forecasting- machine learning- neural networks- neuro-fuzzy systems- pattern recognition- performance measures- self-learning systems- software simulations- supervised and unsupervised learning methods- system engineering and integration. Featured contributions fall into several categories: Original Articles, Review Articles, Book Reviews and Announcements.
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