There's a lot of talk about the benefits of deep learning (neural networks) and how it's the new electricity that will power us into the future. Medical diagnosis, computer vision and speech recognition are all examples of use- cases where neural networks are being applied. This begs the question, what do neural-net applications for cyber security use-cases look like? Specifically how does the process work when applying neural-nets to detect malicious URLs? Follow along as we go from no machine learning knowledge to neural net. Along the way you'll learn what it took to classify URLs as malicious or benign as well as lessons learned directly from our practical attempt at this challenge. Come find out if we had mad success or abject failure; a fun time either way!
Ladi Adefala has served in a variety of strategic technical and leadership roles focused on advanced cyber security. As a FortiGuard Labs cyber security expert with Fortinet, he's engaged in cyber threat intelligence and research efforts. His research interests include cyber threat intelligence and data analytics. He also serves as Adjunct Faculty at Webster University’s Masters of Science – Cyber Security Program, where he engages participating students in the domains of Critical Infrastructure Protection (CIP), network forensics, malware analysis and reverse engineering.