Understanding and Securing Device Vulnerabilities through Automated Bug Report Analysis

Authors: 

Xuan Feng, Beijing Key Laboratory of IOT Information Security Technology, Institute of Information Engineering, CAS, China; School of Cyber Security, University of Chinese Academy of Sciences, China; Xiaojing Liao and XiaoFeng Wang, Department of Computer Science, Indiana University Bloomington, USA; Haining Wang, Department of Electrical and Computer Engineering, University of Delaware, USA; Qiang Li, School of Computer and Information Technology, Beijing Jiaotong University, China; Kai Yang, Hongsong Zhu, and Limin Sun, Beijing Key Laboratory of IOT Information Security Technology, Institute of Information Engineering, CAS, China; School of Cyber Security, University of Chinese Academy of Sciences, China

Abstract: 

Recent years have witnessed the rise of Internet-of-Things (IoT) based cyber attacks. These attacks, as expected, are launched from compromised IoT devices by exploiting security flaws already known. Less clear, however, are the fundamental causes of the pervasiveness of IoT device vulnerabilities and their security implications, particularly in how they affect ongoing cybercrimes. To better understand the problems and seek effective means to suppress the wave of IoT-based attacks, we conduct a comprehensive study based on a large number of real-world attack traces collected from our honeypots, attack tools purchased from the underground, and information collected from high-profile IoT attacks. This study sheds new light on the device vulnerabilities of today's IoT systems and their security implications: ongoing cyber attacks heavily rely on these known vulnerabilities and the attack code released through their reports. We found that the reliance on known vulnerabilities can actually be used against adversaries. The same bug reports that enable the development of an attack at an exceedingly low cost can also be leveraged to extract vulnerability-specific features that help stop the attack. In particular, we leverage Natural Language Processing (NLP) to automatically collect and analyze more than 7,500 security reports (with 12,286 security critical IoT flaws in total) scattered across bug-reporting blogs, forums, and mailing lists on the Internet. We show that signatures can be automatically generated through an NLP-based report analysis, and used by intrusion detection or firewall systems to effectively mitigate the threats from today's IoT-based attacks.

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BibTeX
@inproceedings {236238,
author = {Xuan Feng and Xiaojing Liao and XiaoFeng Wang and Haining Wang and Qiang Li and Kai Yang and Hongsong Zhu and Limin Sun},
title = {Understanding and Securing Device Vulnerabilities through Automated Bug Report Analysis},
booktitle = {28th {USENIX} Security Symposium ({USENIX} Security 19)},
year = {2019},
isbn = {978-1-939133-06-9},
address = {Santa Clara, CA},
pages = {887--903},
url = {https://www.usenix.org/conference/usenixsecurity19/presentation/feng},
publisher = {{USENIX} Association},
month = aug,
}
Feng PDF

Presentation Video