基于android的PPDR终端混合入侵检测系统研究

P. Borges, B. Sousa, Luis Ferreira, Firooz B. Saghezchi, G. Mantas, J. Ribeiro, Jonathan Rodriguez, Luís Cordeiro, P. Simões
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引用次数: 14

摘要

移动设备用于通信和敏感且容易被篡改的任务。实际上,攻击可以在用户不知情的情况下对用户的设备执行,这在关键任务场景(例如公共保护和救灾(PPDR))中代表了额外的风险。入侵检测系统对于信息泄漏至关重要的场景非常重要,因为它们允许检测对信息资产的可能攻击(例如,安装恶意软件),甚至可能危及PPDR人员的安全。HyIDS是一款用于Android的混合型IDS,支持PPDR严格的安全要求,它包括持续监控移动设备的代理,并定期将数据传输到指挥控制中心(CCC)的分析框架。数据收集为每个已安装的应用程序检索资源使用指标,例如CPU、内存使用情况以及传入和传出的网络流量。在CCC, HyIDS采用机器学习技术根据从应用程序收集的数据识别与恶意软件签名一致的模式。HyIDS的评估结果表明,所提出的解决方案在电池消耗和CPU/内存使用方面对移动设备的影响很小。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Towards a Hybrid Intrusion Detection System for Android-based PPDR terminals
Mobile devices are used for communication and for tasks that are sensitive and subject to tampering. Indeed, attacks can be performed on the users' devices without user awareness, this represents additional risk in mission critical scenarios, such as Public Protection and Disaster Relief (PPDR). Intrusion Detection Systems are important for scenarios where information leakage is of crucial importance, since they allow to detect possible attacks to information assets (e.g., installation of malware), or can even compromise the security of PPDR personnel. HyIDS is an Hybrid IDS for Android and supporting the stringent security requirements of PPDR, by comprising agents that continuously monitor mobile device and periodically transmit the data to an analysis framework at the Command Control Center (CCC). The data collection retrieves resource usage metrics for each installed application such as CPU, memory usage, and incoming and outgoing network traffic. At the CCC, the HyIDS employs Machine Learning techniques to identify patterns that are consistent with malware signatures based on the data collected from the applications. The HyIDS's evaluation results demonstrate that the proposed solution has low impact on the mobile device in terms of battery consumption and CPU/memory usage.
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