Advanced threat detection definition
Advanced threat detection (ATD) is a cybersecurity method for finding and stopping cyber threats that standard security measures may miss. It deals with highly targeted attacks, new vulnerabilities, and long-term infiltrations.
Use cases of advanced threat detection
- Protecting against zero-day exploits. Dealing with new vulnerabilities that haven’t been publicly reported or patched.
- Stopping advanced persistent threats (APTs). Detecting and handling sophisticated attacks aimed at stealing data over long periods.
- Preventing data breaches. Spotting unusual data transfers or access to sensitive information.
- Compliance and risk management. Helping organizations meet legal standards by providing advanced levels of security.
- Insider threat detection. Looking for risky actions by authorized users that could threaten security of the organization.
- Securing cloud environments. Enhancing security in cloud-based systems, where traditional measures may fall short.
Components of advanced threat detection
- Behavioral analysis. ATD systems monitor and analyze the behavior of users and network activities. They look for patterns or actions that deviate from the norm, which may be a sign of a threat.
- Machine learning and AI. The system learns from previous incidents and adapts to new types of attacks, improving detection over time.
- Anomaly detection. The system constantly checks for activities that are out of the ordinary. This could include unusual network traffic, unexpected access requests, or strange data movements.
- Threat intelligence. ATD systems use the latest data on known threats, attack methods, and vulnerabilities for better protection.
- Sandboxing. The system tests suspicious programs or files in a safe, isolated space (a sandbox) to avoid risking the main network.
- Integration with other security measures. ATD often works with firewalls, antivirus, and intrusion detection systems for comprehensive security.