信息网络安全 ›› 2016, Vol. 16 ›› Issue (4): 1-8.doi: 10.3969/j.issn.1671-1122.2016.04.001

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基于LKM系统调用劫持的恶意软件行为监控技术研究

丁庸, 曹伟, 罗森林()   

  1. 北京理工大学信息系统及安全对抗实验中心,北京 100081
  • 收稿日期:2016-03-04 出版日期:2016-04-20 发布日期:2020-05-13
  • 作者简介:

    作者简介: 丁庸(1992—),男,山东,硕士研究生,主要研究方向为信息安全;曹伟(1991—),男,安徽,硕士研究生,主要研究方向为信息安全;罗森林(1968—)男,河北,教授,博士,主要研究方向为信息安全、数据挖掘、文本安全。

  • 基金资助:
    国家242信息安全计划[2005C48]

Research on the Technology of Malware Behavior Monitoring Based on LKM System Call Hijacking

Yong DING, Wei CAO, Senlin LUO()   

  1. Information System and Security & Countermeasures Experimental Center, Beijing Institute of Technology, Beijing 100081, China
  • Received:2016-03-04 Online:2016-04-20 Published:2020-05-13

摘要:

Android操作系统是目前设备数最多,使用最为广泛的智能手机操作系统。但Android操作系统在给用户带来方便的同时,其巨大的市场价值也吸引了黑客的目光。以Android恶意软件为主要攻击方式的黑色产业链也逐渐发展壮大,严重危害到了广大智能手机用户的隐私和个人财产安全。因此,针对Android恶意软件检测技术的研究有着极其重要的理论意义和实用价值。文章简述了Android恶意软件相关知识,提出了一种基于LKM的Android应用软件动态行为监控方法。该方法在Linux内核层劫持并替换系统调用,以后台服务的形式运行,可以监控软件发送短信、拨打电话、获取电话号码、网络连接、权限提升等行为。最后,基于该方法设计和实现了软件动态行为监控系统。实验结果表明,该系统对Android软件恶意行为的监控准确率达到了93%,系统性能开销小于5%,具有较高的实用价值。

关键词: 行为监控, LKM, 恶意软件, Android

Abstract:

Android operating system occupies most of the smart devices and has the largest number of users. But smartphone’s huge market value has also attracted the attention of hackers while bringing convenience to users. The black chain which uses malware as the main attack method can put users’ privacy and their property safety in dangerous situation. Therefore, study of the technology on Android malware detection has a very important theoretical value and practical significance. This paper gives a brief introduction on knowledge of Android malware, and proposes an Android application software dynamic behavior monitoring method based on LKM. This method hijacks and replaces the system call in the Linux kernel layer, and later runs in the form of services. It can monitor sending text messages, making phone calls, getting the phone number, network connections, privilege escalation and et al. Experimental results show that the monitoring accuracy rate of malicious behavior reaches to 93% and its performance overhead is less than 5%. Finally, this paper design and implement the dynamic behavior monitoring system based on the method. So, it has a high practical value.

Key words: behavior monitoring, LKM, malware, Android

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