Abstract: DDoS attack detection/prevention is vital across industries to thwart data breaches. Employing tech like data loss prevention systems and fostering security-aware culture through training ...

The anomaly detection module will receive the gathered data and use machine learning (ML) to perform flow-based detection and classify each flow as normal or abnormal. An aberrant flow will be sent to ...

Software-Defined Networking (SDN) has a unique architecture, however it is highly susceptible to Distributed Denial-Of-Service (DDoS) attacks targeting its control and data planes. This paper reviews ...

This project aims to detect Distributed Denial-of-Service (DDoS) attacks using Machine Learning (ML) algorithms. The goal is to classify network traffic as either benign or malicious, helping network ...

Revolutionizing Cybersecurity: Himmat Rathore's Breakthrough in DDoS Detection with Machine Learning and Explainable AI. Manish Saini Published on: 3 October 2024 5:35 pm ...

SDN networks are vulnerable to various security risks, particularly Distributed Denial of Service (DDoS) attacks. To counteract this, we have introduced a model that leverages Machine Learning (ML) to ...

[4] Fatima Khashab, Joanna Moubarak, Antoine Feghali , and Carole Bassil.”DDoS Attack Detection and Mitigation in SDN using Machine Learning”,IEEE 7th International Conference on Network ...