Enhancing Kubernetes Security with AI: Anomaly Detection for Cloud-Based Workloads
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Enhancing Kubernetes Security with AI: Anomaly Detection for Cloud-Based Workloads
Harshad Pitkar1
1Cummins Inc.
Abstract - Kubernetes has become the de facto standard for container orchestration in cloud environments, offering scalability and automation. While its dynamic and complex architecture brings about significant security problems, standard rule-based security mechanisms fail to detect high-level, complex threats. This research proposes an AI-driven anomaly detection framework specifically for Kubernetes security. Multiple data sources such as Kubernetes logs, API calls, network traffic, and system metrics are used in a holistic framework for threat detection. The system uses machine learning models such as Isolation Forest, Autoencoders, and LSTMs to detect deviation from normal behavior, raising the alarm of possible security threats.
Experimental evaluation of the framework shows superior accuracy, recall, and fewer false-positive rates from the ordinary, rule-based security tools. Furthermore, they integrate with Falco, Prometheus, and Open Policy Agent (OPA) to secure monitoring and policy enforcement in Kubernetes clusters. These results show that AI-driven anomaly detection significantly improves the detection of insider threats, zero-day attacks, and other complex security incidents.
However, there are still challenges regarding scalability, explainability, and, in particular, adversarial robustness, although there are clear benefits of CDN transcriptions. Future improvement may include federated learning for distributed threat intelligence, accurate real-time response, and advanced model optimization techniques. Recognizing the need to make Kubernetes more resilient against the new breed of cyber threats, this study puts forward the role of AI-driven security solutions in improving the effectiveness of technology in the modern world.
Key Words: Kubernetes, Cybersecurity, anomaly detection, containers, Cloud Computing
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