EquationX

EquationX explored the convergence of Cybersecurity, Artificial Intelligence, Machine Learning, and Network Security - with a focus on building systems that can intelligently detect, classify, and respond to threats in real time. Problem areas explored: - Intrusion Detection Systems (IDS) augmented with ML - moving beyond signature-based detection to anomaly-based detection that can identify novel attack patterns. - Adversarial robustness in security models - understanding how attackers can fool AI-based security systems and how to harden them. - Network traffic classification - using ML to distinguish benign from malicious traffic at scale, including encrypted traffic analysis. - Automated incident response - exploring how AI agents could triage and respond to security alerts, reducing analyst fatigue. Approach: The team combined literature review, dataset exploration (NSL-KDD, CICIDS), and rapid prototyping to test hypotheses about where ML adds the most value in the cybersecurity stack.

We started with more questions than answers, and somewhere along the way, learned that figuring things out together is half the fun.