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New paper: Distinguishing between malicious app collusion and benign app collaboration: a machine-learning approach

Two or more mobile apps, viewed independently, may not appear to be malicious - but in combination, they could become harmful by exchanging information with one another and by performing malicious activities together. Today, we publish a new paper by a gr…
Yesterday, we published a paper (that was presented at VB2016) on Android app collusions: the situation in which two or more apps work together to exfiltrate data from a device… https://www.virusbulletin.com/blog/2018/03/new-paper-distinguishing-between-malicious-app-collusion-and-benign-app-collaboration-machine-learning-approach/

VB2017 preview: Stuck between a ROC and a hard place

We preview the VB2017 paper by Microsoft's Holly Stewart and Joe Blackbird, which uses data about users switching anti-virus provider to decide whether machine-learning models should favour avoiding false positives over false negatives.
Authors of security software in general, and anti-virus software in particular, have always needed to find the right balance between a high detection rate and a low false positive… https://www.virusbulletin.com/blog/2017/08/vb2017-preview-stuck-between-roc-and-hard-place/

Research paper shows it may be possible to distinguish malware traffic using TLS

Researchers at Cisco have published a paper describing how it may be possible to use machine learning to distinguish malware command-and-control traffic using TLS from regular enterprise traffic, and to classify malware families based on their encrypted C…
Researchers at Cisco have published a paper (PDF) describing how it may be possible to use machine learning to distinguish malware command-and-control (C&C) traffic using TLS from… https://www.virusbulletin.com/blog/2017/06/research-paper-shows-it-may-be-possible-distinguish-malware-traffic-using-tls/