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Creators/Authors contains: "Sun, L."

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  1. ; ; ; ; ( , SIGIR)
    Free, publicly-accessible full text available July 1, 2022
  2. ; ; ; ; ; ; ( , IEEE transactions on mobile computing)
    Free, publicly-accessible full text available April 1, 2022
  3. ; ; ; ; ; ; ( , Proceedings of the AAAI Conference on Artificial Intelligence)
    Free, publicly-accessible full text available February 1, 2022
  4. ; ; ; ; ; ; ( , IEEE International Conference on Data Mining)
    Accepted Manuscript Full Text Available
  5. ; ; ; ; ; ( , ACM CIKM)
    Full Text Available 
  6. ; ; ( , Environmental Research Letters)
    Full Text Available 
  7. ; ; ; ; ; ; ; ; ; ; et al ( , Physical Review C)
    Full Text Available 
  8. ; ; ; ; ; ; ; ; ; ; et al ( , Physical Review C)
    Full Text Available 
  9. ; ; ; ; ; ( , IEEE Big Data)
    Full Text Available 
  10. ; ; ; ; ; ; ; ( , SEC'19: Proceedings of the 28th USENIX Conference on Security Symposium)
    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.more »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.« less
    Accepted Manuscript Full Text Available