Machine learning and code to detect and alert attempts to extract passwords from staff

Security researchers from UC Berkeley and the Lawrence Berkeley National Laboratory in the US have come up with a way to mitigate the risk of spear-phishing in corporate environments.

In a paper presented at Usenix 2017, titled "Detecting Credential Spearphishing in Enterprise Settings," Grant Ho, Mobin Javed, Vern Paxson, and David Wagner from UC Berkeley, and Aashish Sharma of The Lawrence Berkeley National Laboratory (LBNL), describe a system that utilizes network traffic logs in conjunction with machine learning to provide real-time alerts when employees click on suspect URLs embedded in emails.

Spear-phishing is a social engineering attack that involves targeting specific individuals with email messages designed to dupe the recipient into installing a malicious file or visiting a malicious website.

Such targeted attacks are less common than phishing attacks launched without a specific victim in mind, but they tend to be more damaging.

High profile data thefts at the Office of Personnel Management (22.1 million people) and at health insurance provider Anthem (80 million patient records), among others, have involved spear-phishing.

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