Detection of Intrusions and Malware, and Vulnerability Assessment

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On the (Statistical) Detection of Adversarial Examples (2017, background): “Finally, we want to remark that the benign data, that is labeled as outlier by the network might be beneficial when investigated by an expert[23].”
Toward Supervised Anomaly Detection (2014, methodology): “Irrespectively of the amount of labeled data, the malicious trafﬁc is detected by all methods equally well as the intrinsic nature of the attacks is well captured by the bag-of-3-grams representation (cf., Wang, Parekh, & Stolfo, 2006; Rieck & Laskov, 2006).”
A blockchain future for internet of things security: a position paper (2017, background): “Freudiger, Cristofaro and Brito [90] proposed a controlled data sharing approach on collaborative predictive blacklisting for collaborative threat mitigation.”
UNVEIL: A large-scale, automated approach to detecting ransomware (keynote) (2016, methodology): “Later work focused on analyzing and detecting malware using higher-level semantic characterizations of their runtime behavior derived from sequences of system call invocations and OS resource accesses [24, 25, 11, 33, 42, 51].”