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Deep-Learning–Based App Sensitive Behavior Surveillance for Android Powered Cyber–Physical Systems

机译：基于深度学习的Android动力网络物理系统的应用敏感行为监控

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摘要

Android as an operating system is now increasingly being adopted in industrial information systems, especially with cyber-physical systems (CPS). This also puts Android devices onto the front line of handling security-related data and conducting sensitive behaviors, which could be misused by the increasing number of polymorphic and metamorphic malicious applications targeting the platform. The existence of such malware threats, therefore, call for more accurate identification and surveillance of sensitive Android app behaviors, which is essential to the security of CPS and Internet of Things (IoT) devices powered by Android. Nevertheless, achieving dynamic app behavior monitoring and identification on real CPS powered by Android is challenging because of restrictions from the security and privacy model of the platform. In this article, the authors investigate how the latest advances in deep learning could address this security problem with better accuracy. Specifically, a deep learning engine is proposed that detects sensitive app behaviors by classifying patterns of system-wide statistics, such as available storage space and transmitted packet volume, using a customized deep neural network based on existing models called Encoder and ResNet. Meanwhile, to handle resource limitations on typical CPS and IoT devices, sparse learning is adopted to reduce the amount of valid parameters in the trained neural network. Evaluations show that the proposed model outperforms a well-established group of baselines on time series classification in identifying sensitive app behaviors with background noise and the targeted behaviors potentially overlapping.
机译：Android作为操作系统现在正在越来越多地在工业信息系统中采用，尤其是网络物理系统（CPS）。这也将Android设备放在处理安全相关数据的前线并进行敏感行为，这可能被越来越多的多态性和定位平台的变质恶意应用程序滥用。因此，存在这种恶意软件威胁，请呼吁更准确地识别和监视敏感的Android应用行为，这对于CPS和Internet的安全性（IOT）设备是必不可少的。然而，由于平台的安全和隐私模型的限制，实现了Android的真实CPS的动态应用程序行为监测和识别是具有挑战性的。在本文中，作者调查了深度学习的最新进步如何以更好的准确性解决这种安全问题。具体地，提出了一种深入学习引擎，其通过基于称为编码器和Reset的现有模型对诸如可用存储空间和传输的分组体积的系统范围统计和传输的分组卷进行分类来检测敏感的应用程序行为。同时，为了处理典型的CP和IOT设备上的资源限制，采用稀疏学习来减少培训的神经网络中的有效参数的量。评估表明，该模型在识别具有背景噪声的敏感应用行为和潜在重叠的目标行为的时间序列分类中占据了一系列成熟的基线组。

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