Cyber Threat Detection

This application area focuses on detecting malicious activity in networks, systems, and applications by analyzing security telemetry such as logs, network flows, and endpoint events. Instead of relying solely on static signatures and manual rules, these systems learn patterns of normal and abnormal behavior to identify intrusions, malware, lateral movement, and other cyber-attacks in real time or near real time. They are typically implemented in or alongside intrusion detection systems (IDS), SIEMs, and modern security analytics platforms. It matters because traditional rule-based tools struggle with the scale, speed, and evolving nature of today’s threats, leading to high false positives, missed novel attacks, and analyst overload. Advanced models—ranging from classical machine learning to deep learning, transformers, and large language models—are used to improve detection accuracy, adapt to new attack techniques, correlate signals across large, noisy data sets, and automate parts of triage and response. The result is more effective, timely detection with less manual effort for security teams.