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" An Empirical Analysis on Threat Intelligence: "
Li, Guo
Savage, Stefan; Levchenko, Kirill
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Latin Dissertation
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| Language of Document
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English
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| Record Number
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905850
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| Doc. No
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TL5h9983b0
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| Main Entry
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Li, Guo
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| Title & Author
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An Empirical Analysis on Threat Intelligence:\ Li, GuoSavage, Stefan; Levchenko, Kirill
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| College
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UC San Diego
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| Date
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2020
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| student score
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2020
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| Abstract
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Threat Intelligence, both as a concept and a product, has been increasingly gaining prominence in the security industry. At a high-level, it is the “knowledge” that helps organizations understand and mitigate cyber-attacks. Most commonly, it refers to the collection of threat indicators—IP addresses, domain names, file hashes, etc. known to be associated with attacks. By compiling and distributing this information, it is believed that recipients will be able to better defend their systems from future attacks. Thus, there are now hundreds of vendors offering their threat intelligence solutions as a mix of public and commercial products.However, our understanding of this data, its characterization, and the extent to which it can meaningfully support its intended uses, is still quite limited. Furthermore, how the data is being used by organizations, how popular it is, and what impact it could have on the Internet are also not clear to our community. We lack an empirical assessment of real-world threat intelligence, both in terms of the data itself and its usage, and it is important to first understand the current status of threat intelligence, then can we reasonably discuss how to make improvements.In this dissertation, I take an empirical approach to study threat intelligence and try to address these gaps. In particular, I explore this topic from two perspectives: 1) Studying the characteristics of threat intelligence data itself and 2) Exploring how they are used in the real-world. In particular, I formally defined a set of metrics for analyzing threat intelligence data feeds and use these measures to systematically evaluate a broad range of public and commercial feeds. Further, I ground my quantitative assessments using external measurements to investigate issues of coverage and accuracy. Finally, I designed a method using the IP ID side channel to test if a remote host is blocking traffic from a given IP address. Using this technique, I measured over 220K U.S. hosts and tested whether they consistently block connections with IPs identified on popular IP blacklists. Beyond these blacklists, I also demonstrate the evidence for more widespread use of security related traffic blocking. Together, my work provides an in-depth look into the current picture of threat intelligence and augments the knowledge of our community on this topic.
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Savage, Stefan; Levchenko, Kirill
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| Added Entry
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UC San Diego
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