Battling against Protocol Fuzzing: Protecting Networked Embedded Devices from Dynamic Fuzzers

Author:

Liu Puzhuo1ORCID,Zheng Yaowen2ORCID,Sun Chengnian3ORCID,Li Hong4ORCID,Li Zhi1ORCID,Sun Limin1ORCID

Affiliation:

1. Beijing Key Laboratory of IoT Information Security Technology, Institute of Information Engineering, CAS; School of Cyber Security, University of Chinese Academy of Sciences, Beijing, China

2. Nanyang Technological University, Singapore, Singapore

3. Cheriton School of Computer Science, University of Waterloo, Waterloo, Canada

4. Institute of Information Engineering, Chinese Academy of Sciences, Beijing; School of Cyber Security, University of Chinese Academy of Sciences, Beijing, China

Abstract

N etworked E mbedded D evices (NEDs) are increasingly targeted by cyberattacks, mainly due to their widespread use in our daily lives. Vulnerabilities in NEDs are the root causes of these cyberattacks. Although deployed NEDs go through thorough code audits, there can still be considerable exploitable vulnerabilities. Existing mitigation measures like code encryption and obfuscation adopted by vendors can resist static analysis on deployed NEDs, but are ineffective against protocol fuzzing. Attackers can easily apply protocol fuzzing to discover vulnerabilities and compromise deployed NEDs. Unfortunately, prior anti-fuzzing techniques are impractical as they significantly slow down NEDs, hampering NED availability. To address this issue, we propose Armor—the first anti-fuzzing technique specifically designed for NEDs. First, we design three adversarial primitives–delay, fake coverage, and forged exception–to break the fundamental mechanisms on which fuzzing relies to effectively find vulnerabilities. Second, based on our observation that inputs from normal users consistent with the protocol specification and certain program paths are rarely executed with normal inputs, we design static and dynamic strategies to decide whether to activate the adversarial primitives. Extensive evaluations show that Armor incurs negligible time overhead and effectively reduces the code coverage (e.g., line coverage by 22%-61%) for fuzzing, significantly outperforming the state of the art.

Funder

National Key R&D Program of Ministry of Science and Technology

Natural Science Foundation of Beijing

Publisher

Association for Computing Machinery (ACM)

Reference74 articles.

1. AFL. 2020. Accessed 2022-10-20. https://github.com/google/AFL

2. AFL++. 2022. Accessed 2022-10-20. https://github.com/AFLplusplus/AFLplusplus

3. Ross Anderson and Tyler Moore. 2007. Information security economics–and beyond. In Annual International Cryptology Conference. Springer, 68–91.

4. Resurrecting anti-virtualization and anti-debugging: Unhooking your hooks;Apostolopoulos Theodoros;Future Generation Computer Systems,2021

5. Jinsheng Ba, Marcel Böhme, Zahra Mirzamomen, and Abhik Roychoudhury. 2022. Stateful greybox fuzzing. In 31st USENIX Security Symposium (USENIX Security’22). 3255–3272.

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3