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In today’s cloud-native environments, network security is more complex than ever, with Kubernetes and containerized workloads introducing unique challenges. Traditional tools struggle to monitor and secure these dynamic, interconnected systems, leaving organizations vulnerable to advanced threats, such as lateral movement, zero-day exploits, ransomware, data exfiltration, and more.
Network threat detection identifies malicious or suspicious activity within network traffic by using rules and analyzing patterns, behaviors, and anomalies. It enables organizations to spot attacks early, respond quickly, and mitigate risks before they escalate. Tools like Calico are specifically designed to address these challenges in Kubernetes, offering visibility, detection, and automated responses to protect workloads from known and emerging threats.
Calico delivers advanced network threat detection for Kubernetes environments, leveraging a variety of techniques to ensure comprehensive protection. Here are the key features of Calico’s network threat detection.
Behavior-based detection
Calico uses machine learning algorithms to establish a baseline of normal network behavior and detect anomalies such as port scans, IP (Internet Protocol) sweeps, and domain generation algorithms (DGA), which are commonly used by malware to evade detection and maintain communication with command and control (C2) servers.
Calico’s anomaly detection capability evaluates traffic flows using machine learning to identify the baseline behavior Continue reading