"Columbia Professor Develops a Detector That Stops Lateral Phishing Attacks"

There has been increasing concern about the rise in lateral phishing attacks. In a lateral phishing scheme, attackers compromise legitimate email accounts inside an organization, which are then used to send phishing emails to employees within that organization. It is harder for existing email security systems to detect and stop internal phishing emails as these systems look at signals such as IP and domain reputation. According to the FBI, organizations have faced a total of more than $12 billion in losses between 2013 and 2018 because of such cyberattacks. To address this problem, Asaf Cidon and other members of the Data Science Institute at Columbia developed a machine-learning based detector to stop lateral phishing attacks. This article continues to discuss the concept and impact of lateral phishing attacks, as well as the detector developed by researchers to stop these targeted socially-engineered attacks.

Science Daily reports  "Columbia Professor Develops a Detector That Stops Lateral Phishing Attacks"