Anomaly detection in cybersecurity involves identifying patterns or behaviors that deviate from the norm. This process usually starts with the establishment of a baseline, which is a representation of normal network activity, user behavior, or system performance. By analyzing historical data, security systems can learn what typical activity looks like. Once this baseline is set, any significant deviation from it—such as unusual login times, unexpected data transfers, or irregular access to certain files—can be flagged as potential security threats.
The detection methods can vary, but common techniques include statistical analysis, machine learning, and rule-based detection. For example, a statistical approach might use standard deviation to determine what constitutes normal behavior and flag any activity that falls outside of a certain range. Machine learning models can be trained on vast datasets to recognize complex patterns and adapt as the nature of normal behavior evolves. In contrast, rule-based systems rely on predefined criteria that trigger alerts when certain conditions are met, such as multiple failed login attempts from the same IP address.
Anomaly detection plays a critical role in identifying intrusions, insider threats, and data breaches. For instance, if a user typically accesses documents during business hours but suddenly begins downloading large quantities of sensitive data late at night, this could indicate a compromised account. Similarly, if a network that usually sees a certain level of traffic suddenly experiences a spike, this could point to a denial-of-service attack. By effectively detecting such anomalies, cybersecurity professionals can take proactive steps to mitigate potential risks and protect their systems.