Machine Learning Based on Classification for Detection of DDoS Attacks in Cloud Computing
DDoS | Machine learning
Vol: 8; Iss:1; Jan 22 | ISSN: 2454-5422
DDoS (Distributed Denial of Service) is a network security assault, and attackers have now infiltrated practically every technology, including cloud computing, IoT, and edge computing, to strengthen themselves. According to DDoS behaviour, the attacker consumes all available resources such as memory, CPU, and perhaps the entire network in order to bring down the victim’s system or server. Though several defensive mechanisms have been developed, they are ineffective since attackers are educated by the newly accessible automated assaulting tools. As a result, we suggested a machine learning strategy based on categorization for detecting DDoS attacks in cloud computing. The mechanism can identify a DDoS assault with accuracy using three classification machine learning algorithms: K Nearest Neighbor, Random Forest, and Naive Bayes.