BotGrep: Finding P2P Bots with Structured Graph Analysis

By Shishir Nagaraja, Prateek Mittal, C. Hong, Matthew C. Caesar, Nikita Borisov. USENIX Security Symposium, 2010

313 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/6e39b04b21ca33790071c1458e983e21170e392d

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Citations in the corpus

Strathprints Institutional Repository (unknown): “For example, PSI has been proposed as a building block in applications such as privacy preserving data mining [4], human genome research [6], homeland security [15], Botnet detection [34], social networks [33], location sharing [36] and cheater detection in online games [11].”
SybilBelief: A Semi-Supervised Learning Approach for Structure-Based Sybil Detection (2013, methodology): “Interesting avenues for future work include evaluating SybilBelief and previous approaches with datasets containing real Sybils and applying our SybilBelief framework to other security and privacy problems such as graph based Botnet detection [44], reputation systems [37], and private information in”
Faster Private Set Intersection Based on OT Extension (2014, methodology): “PSI is used for privacy-preserving computation of functionalities such as relationship path discovery in social networks [41], botnet detection [45], testing of fully-sequenced human genomes [3], proximity testing [48], or cheater detection in online games [11].”
On the Security of Machine Learning in Malware C&C Detection (2016, background): “Both of these approaches were successful in identifying multiple new servers for certain malware families, with 0% FP rates.”
Scalable Private Set Intersection Based on OT Extension (2018, background): “…privacy-preserving computation of functionalities such as relationship path discovery in social networks [Mezzour et al. 2009], botnet detection [Nagaraja et al. 2010], testing of fully-sequenced human genomes [Baldi et al. 2011], proximity testing [Narayanan et al. 2011], or cheater detection…”
PeerRush: Mining for unwanted P2P traffic (2013, background): “Also, a number of previous studies have focused on detecting P2P botnets [5, 22, 15, 2, 23], but with little or no attention to accurately distinguishing between different types of P2P botnet families based on their P2P traffic patterns.”
Reliable Client Accounting for Hybrid Content-Distribution Networks (2012, unknown): “, in BotGrep [26].”
More efficient oblivious transfer and extensions for faster secure computation (2013, unknown): “Applications include governmental law enforcement [9], sharing location data [41], and botnet detection [39].”