A Lightweight Intrusion Prevention System for Edge and Cloud-Integrated Networks
Abstract
As cloud computing extends to the edge, securing edge-cloud integrated networks has become a pressing challenge. This paper proposes a lightweight Intrusion Prevention System (IPS) designed for edge-cloud architectures. The IPS uses lightweight machine learning models to detect and block malicious activities in real-time while minimizing computational overhead. It features a distributed architecture that allows edge nodes to independently process traffic and share threat intelligence with the cloud. Performance tests show that the IPS achieves low latency and high detection accuracy, even under high traffic loads. The study concludes that lightweight IPS solutions are essential for securing next-generation cloud and edge networks.
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