Authors: OM Kute, Yuvraj Narwade, T.B. Faruki
Abstract: Denial of Service (DoS) attacks are one of the most common cyber threats that disrupt network services by overwhelming systems with malicious traffic. Traditional cloud-based detection methods often experience higher latency and increased bandwidth usage, making them less effective for real-time protection. The DoS Attack Detection System Using Edge Machine Learning introduces an intelligent approach that detects malicious network traffic directly at edge devices before it reaches the central server. By leveraging Edge Computing, Machine Learning, and real-time traffic analysis, the system identifies abnormal network behavior with low latency and improved accuracy. This approach reduces server overload, enhances network security, and ensures continuous availability of services while providing a scalable and efficient solution for modern IoT and edge-enabled environments.
