Challenge: In the late 2000s, the client sought an advanced cybersecurity solution to detect and respond to anomalies in their network. The goal was to develop a system that could establish normal behavior baselines and identify significant deviations in real-time, akin to modern Security Information and Event Management (SIEM) systems.
Solution: A sophisticated ML-based baselining and threat-hunting system was developed. The solution leveraged logs from multiple sources including firewalls, IDS/IPS systems, routers, servers, and Netflow traffic. The system established baselines representing "normal" behavior and monitored these baselines in real-time to detect anomalies.
Implementation and Results:
Baseline Establishment: The system analyzed logs from various sources to establish normal behavior baselines, which would be then used for comparison later on to detect anomalous behavior.
Real-Time Monitoring: The system continuously monitored network activity against the established baselines. Significant deviations were detected in real-time, enabling prompt identification of anomalies.
Rapid Alerts and Automated Responses: Administrators were alerted within 30 minutes of detecting an anomaly via both email and SMS gateway integration. Additionally, automated threat responses were configured based on past experiences and threat intelligence, ensuring swift action against potential threats.
Impact: The implementation of this ML-based cybersecurity solution significantly enhanced the client's ability to detect and respond to threats. By automating threat detection and response, the system reduced the time to identify and mitigate potential security incidents, thereby strengthening the overall security posture.
This case study demonstrates the effectiveness of early ML-based threat-hunting systems in cybersecurity, highlighting the ability to proactively identify and respond to anomalies, much like modern SIEM solutions.
