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; on the other hand,
such a 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 be used by intrusion detection or firewall systems
to effectively mitigate the threats from today’s IoT-based
attacks.
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Security Vulnerability Detection Using Deep Learning Natural Language Processing
Detecting security vulnerabilities in software before they are exploited has been a challenging problem for decades. Traditional code analysis methods have been proposed, but are often ineffective and inefficient. In this work, we model software vulnerability detection as a natural language processing (NLP) problem with source code treated as texts, and address the auto-mated software venerability detection with recent advanced deep learning NLP models assisted by transfer learning on written English. For training and testing, we have preprocessed the NIST NVD/SARD databases and built a dataset of over 100,000 files in C programming language with 123 types of vulnerabilities. The extensive experiments generate the best performance of over 93% accuracy in detecting security vulnerabilities.
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- Award ID(s):
- 2109971
- NSF-PAR ID:
- 10287750
- Date Published:
- Journal Name:
- IEEE INFOCOM WKSHPS: BigSecurity 2021: International Workshop on Security and Privacy in Big Data
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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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; on the other hand, such a 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 be used by intrusion detection or firewall systems to effectively mitigate the threats from today's IoT-based attacks.more » « less
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