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Research Spotlight: Needles in a Haystack

- January 14, 2016 - 1 Comment

This post was authored by Mariano Graziano.

Malware sandboxes are automated dynamic analysis systems that execute programs in a controlled environment. Within the large volumes of samples submitted daily to these services, some submissions appear to be different from others and show interesting characteristics. At USENIX Security 2015 I presented a paper in which we proposed a method to automatically discover malware developments from samples submitted to online dynamic analysis systems. The research was conducted by dissecting the Anubis sandbox dataset which consisted of over 30M samples collected in six years. The methodology we proposed was effective and we were able to detect many interesting cases in which the malware authors directly interacted with the sandbox during the development phase of the threats.

Another interesting result that came from the research concerns the samples attributed to Advanced Persistent Threat (APT) campaigns. Surprisingly, some of the malware samples used in these sophisticated attacks had been submitted to the Anubis sandbox months — sometimes even years — before the attack had been attributed to the proper APT campaign by a security vendor. To be perfectly clear, we are not saying that it took security vendors months or years to detect a threat. Most times, we are able to detect the  threats in no more than a few hours. It is just that the malware samples were mislabeled and not properly associated with APT campaigns. In general, the same goes for non-APT malware campaigns. In this blog post, we tried to see if the same applied to the Cisco dataset. Specifically, we chose ten APT campaigns, — some of which were already covered in the Usenix paper. We decided to inspect two different datasets: our incoming sample feeds / malware zoo, and the telemetry associated with our Advanced Malware Protection (AMP) solutions. Talos receives samples from over 100 external feeds ranging from anti-malware companies to research centers, while the AMP dataset contains telemetry from the Cisco AMP user-base.

The remaining part of this post is organized as follows. First, we show the APT campaigns we investigated. Second, we summarize the results of the analysis of the Talos dataset. Third, we show the results from the AMP dataset.  Finally, we summarize our findings.


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    Very detailed study to discover malware and the best approach to decrease threats.