Weaponizing Data Science for Social Engineering by John Seymour

In the security field, we have this idea of offensive security. It means to build malicious capabilities out before attackers do, so we can learn to defend against them. And in fact, at the NIPS Workshop on Machine Deception, my co-author and I brought the idea of using machine learning for spear-phishing back to the machine learning community. We showed that it’s actually very easy for attackers to use machine learning in order to drastically increase click-through rates of phishing links — possibly up to as high as 66%.

Combining the effectiveness of spear-phishing and the automation of regular phishing is possible using generative models and features from social media profiles. There are many ways to do this, but the implementation we demonstrated is split into two components: a machine learning component to detect whether a given user is (un)likely to click a link, and another machine learning component to generate text geared specifically for that user.

Because we don’t have labeled data for users as to what links they click,....

Author

Hiep Nguyen

• BlogSeptember 23, 2023Leveraging AI in Cybersecurity: Transforming Threat Detection, Prevention, and Beyond
• BlogAugust 24, 2023How Simply Browsing The Internet Gives Scammers An Advantage – And What You Can Do About It
• BlogJuly 1, 2022WEF - WiFi Exploitation Framework
• BlogMay 19, 2022Osmedeus is a Workflow Engine for Offensive Security