botscan

Botscan is a software system designed to detect and analyze automated bot activity across networks and online services. It collects and correlates data from network telemetry, application logs, and threat intelligence to identify behavior indicative of bots, such as rapid or synchronized requests, unusual login patterns, or known command-and-control communication.

Core components include traffic analysis, anomaly detection, device fingerprinting, and reputation checks, with event correlation to distinguish bots from human users. It can operate as a network sensor, host agent, or cloud service, and typically integrates with security information and event management systems and incident response workflows.

Bot detection methods rely on a combination of protocol analysis, timing and sequencing patterns, and interaction with decoy resources such as honeypots. Some implementations also incorporate machine learning classifiers to adapt to evolving bot tactics and to reduce false positives.

Botscan is used by enterprises to protect web applications and APIs, by service providers to mitigate abusive bot traffic, and by researchers studying bot behavior. Deployments vary from on-premises sensors to fully managed cloud services, and they often feed data into SIEMs, threat intelligence platforms, and access controls.

Historically, botscan-like systems emerged from botnet detection and bot management research, progressing from signature-based detectors to behavior-based and ML-driven approaches as bot operators adopted encryption and fast-changing protocols.

Limitations include the potential for false positives, encrypted or obfuscated traffic, evasion by adversaries, and privacy considerations, as well as performance overhead on networks with high traffic.

See also: Botnet detection, Web security, Bot management.