Description: Anomaly detection in traffic refers to the identification of unusual patterns in the flow of data traversing a network. This process is crucial for cybersecurity, as it allows for the detection of suspicious activities that may indicate a cyberattack or security breach. Anomalies can manifest in various forms, such as a sudden increase in traffic volume, unauthorized access to critical resources, or behavior patterns that differ from the established norm. Anomaly detection relies on data analysis techniques and machine learning, enabling systems to learn from normal traffic patterns and thus identify significant deviations. This capability is particularly relevant in the context of DDoS attack protection, where an unexpected increase in traffic may indicate an ongoing attack. Similarly, in the realm of network security, anomaly detection becomes an essential tool for safeguarding communication integrity. Intrusion Detection Systems (IDS) and Intrusion Prevention Systems (IPS) also utilize this technique to monitor and respond to threats in real-time, ensuring that networks remain secure against an ever-evolving threat landscape.
