违规行为:4G LTE蜂窝设备的自动黑箱违规检查

Syed Rafiul Hussain, Imtiaz Karim, Abdullah Al Ishtiaq, Omar Chowdhury, E. Bertino
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引用次数: 14

摘要

本文的重点是开发一种名为DIKEUE的自动化黑盒测试方法,用于检查商用现货(COTS)蜂窝设备(也称为用户设备或ue)中的4G长期演进(LTE)控制平面协议实现是否符合标准。与之前依赖于属性引导测试的不合规检查方法不同,DIKEUE采用了一种属性不可知的差异测试方法,它利用了COTS ue中存在的许多不同的控制平面协议实现。DIKEUE使用在成对COTS ue的差异分析中观察到的异常行为作为识别不合规实例的代理。对于异常行为识别,DIKEUE首先使用专用于4G LTE控制平面协议的黑盒自动学习来提取给定UE的输入输出有限状态机(FSM)。然后,它减少了对两个提取的fsm中异常行为的识别,作为一个模型检查问题。我们应用DIKEUE检查了来自5个供应商的14个COTS ue的不合规情况,并确定了15个新的异常行为以及2个以前的实施问题。其中11个是可利用的,而3个可能导致潜在的互操作性问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Noncompliance as Deviant Behavior: An Automated Black-box Noncompliance Checker for 4G LTE Cellular Devices
The paper focuses on developing an automated black-box testing approach called DIKEUE that checks 4G Long Term Evolution (LTE) control-plane protocol implementations in commercial-off-the-shelf (COTS) cellular devices (also, User Equipments or UEs) for noncompliance with the standard. Unlike prior noncompliance checking approaches which rely on property-guided testing, DIKEUE adopts a property-agnostic, differential testing approach, which leverages the existence of many different control-plane protocol implementations in COTS UEs. DIKEUE uses deviant behavior observed during differential analysis of pairwise COTS UEs as a proxy for identifying noncompliance instances. For deviant behavior identification, DIKEUE first uses black-box automata learning, specialized for 4G LTE control-plane protocols, to extract input-output finite state machine (FSM) for a given UE. It then reduces the identification of deviant behavior in two extracted FSMs as a model checking problem. We applied DIKEUE in checking noncompliance in 14 COTS UEs from 5 vendors and identified 15 new deviant behavior as well as 2 previous implementation issues. Among them, 11 are exploitable whereas 3 can cause potential interoperability issues.
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