Automated Bot-Nets Coordination Discovery (ABCD) Designed for Distortion Campaigns and Influence Operations

Today, information warfare is conducted by AI–driven BOT-NETS that are designed, developed, and often supervised by state and non-state actors (i.e. “cyborg” information actors) to spread propaganda, distort campaigns, and spread dis-information. Such distortion campaigns are conducted in multiple languages across a wide range of social media platforms. There is a need for new technologies to detect “cyborg” accounts, sophisticated fake accounts, and systems of coordinated botnets, dormant bots, and of “weaponized botnets” – botnets currently operating that latch on to crisis situations and high-flowing trends to infiltrate and steer online conversations and initial assessment of their activities. To address this need, we propose to develop and implement a suite of algorithms for Automated Bot-Nets Coordination Discovery (i.e. ABCD analytics) that go beyond current botnet detection capabilities to create algorithms that can distinguish patterns of botnet driven and stochastic manipulation activity and identify associations among botnets and cyborg accounts. We will develop and extend BOT-NET detection capabilities on platforms such as VK (i.e. Russian language) and Weibo (i.e. Chinese language). We plan to demonstrate a working prototype of ABCD analytics working on VK and Weibo posts in Scraawl.com by end of Phase I task period.