Fast and Reliable DDoS Detection using Dimensionality Reduction and Machine Learning
Zein Ashi, Laila Aburashed, Mohammad Al-Fawa’reh, Malek Qasaimeh · 2020
Distributed Denial of Service (DDoS) Attack poses a rising threat on cloud computing systems in which the attacker exploits machines from outside and inside the cloud system to initiate the attack against. To prevent DDoS attack, real-time analysis of the cloud network traffic is fundamental. Machine learning techniques are an effective solution to develop a robust Intrusion detection system in cloud computing systems. This paper proposed a machine learning framework, explores the possibility of utilizing a machine learning classifier to detect the DDoS attack on cloud computing systems; first by the full dimensions of the features, second by reducing these dimensions. Our framework is characterized by a high accurate rate in detecting emerging DDoS attacks, and its lightweight algorithm.