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SensorBuster: On Identifying Sensor Nodes in P2P Botnets

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Published:29 August 2017Publication History

ABSTRACT

The ever-growing number of cyber attacks originating from botnets has made them one of the biggest threat to the Internet ecosystem. Especially P2P-based botnets like ZeroAccess and Sality require special attention as they have been proven to be very resilient against takedown attempts. To identify weaknesses and to prepare takedowns more carefully it is thus a necessity to monitor them by crawling and deploying sensor nodes. This in turn provokes botmasters to come up with monitoring countermeasures to protect their assets. Most existing anti-monitoring countermeasures focus mainly on the detection of crawlers and not on the detection of sensors deployed in a botnet. In this paper, we propose two sensor detection mechanisms called SensorRanker and SensorBuster. We evaluate these mechanisms in two real world botnets, Sality and ZeroAccess. Our results indicate that SensorRanker and SensorBuster are able to detect up to 17 sensors deployed in Sality and four within ZeroAccess.

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      • Published in

        cover image ACM Other conferences
        ARES '17: Proceedings of the 12th International Conference on Availability, Reliability and Security
        August 2017
        853 pages
        ISBN:9781450352574
        DOI:10.1145/3098954

        Copyright © 2017 ACM

        © 2017 Association for Computing Machinery. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 29 August 2017

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        • short-paper
        • Research
        • Refereed limited

        Acceptance Rates

        ARES '17 Paper Acceptance Rate100of191submissions,52%Overall Acceptance Rate228of451submissions,51%

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