On the Security of Machine Learning in Malware C&C Detection

By Joseph Gardiner, Shishir Nagaraja. 2016

127 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Signal Processing

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Citations in the corpus

Stronger data poisoning attacks break data sanitization defenses (2018, background): “(2014), Gardiner and Nagaraja (2016), Papernot et al.”
Certified Defenses for Data Poisoning Attacks (2017, background): “Due to their increased use in security-critical settings such as malware detection, there has been an explosion of work on the security of machine learning systems; see Barreno et al. (2010), Biggio et al. (2014a), Papernot et al. (2016b), and Gardiner and Nagaraja (2016) for some recent surveys.”
Certified Defenses against Adversarial Examples (2018, background): “There has been a great deal of recent work on the security of ML systems; we provide only a sampling here, and refer the reader to Barreno et al. (2010), Biggio et al. (2014a), Papernot et al. (2016b), and Gardiner & Nagaraja (2016) for some recent surveys.”
Survey on the Usage of Machine Learning Techniques for Malware Analysis (2017, background): “Gardiner and Nagaraja [10] concentrate their survey on the detection of command and control centres through machine learning.”
PlatPal: Detecting Malicious Documents with Platform Diversity (2017, background): “However, recently proposed adversarial machine learning techniques [20, 42, 65] raise serious doubts about the effectiveness of classifiers based on superficial features in the presence of adversaries.”
Robust Malware Detection for Internet of (Battlefield) Things Devices Using Deep Eigenspace Learning (2019, background): “Intrusion and malware detection and prevention are two active research areas [1], [2], [3], [4], [5].”
Towards Adversarial Malware Detection (2018, methodology): “mework and described them by also sketching possible novel strategies that can be employed by attackers. To this end, we employed a methodology similar to previous work on command-and-control botnets [37]. However, PDF malware detection is clearly a different task and, to the best of our knowledge, ”