Visible to the public DDoS Attacks Detection by Using Machine Learning Methods on Online Systems

TitleDDoS Attacks Detection by Using Machine Learning Methods on Online Systems
Publication TypeConference Paper
Year of Publication2020
AuthorsBaşkaya, D., Samet, R.
Conference Name2020 5th International Conference on Computer Science and Engineering (UBMK)
Date Publishedsep
KeywordsC-support vector machine, composability, Computer crime, computer network security, Cyber Attacks, cyber security, DDoS, DDoS attack detection, DDOS attacks detection, denial-of-service attack, detection accuracy rates, distributed denial of service attacks, Global Positioning System, Hafnium compounds, Handheld computers, HTTP flood, Human Behavior, hypermedia, hypertext transfer protocol, Intrusion detection, k-nearest neighbor, learning (artificial intelligence), machine learning, Metrics, multi layer perceptron, multilayer perceptrons, nearest neighbour methods, online systems, pubcrawl, Random Forest, random forests, resilience, Resiliency, Support vector machines, TCP SYN flood, Topology
AbstractDDoS attacks impose serious threats to many large or small organizations; therefore DDoS attacks have to be detected as soon as possible. In this study, a methodology to detect DDoS attacks is proposed and implemented on online systems. In the scope of the proposed methodology, Multi Layer Perceptron (MLP), Random Forest (RF), K-Nearest Neighbor (KNN), C-Support Vector Machine (SVC) machine learning methods are used with scaling and feature reduction preprocessing methods and then effects of preprocesses on detection accuracy rates of HTTP (Hypertext Transfer Protocol) flood, TCP SYN (Transport Control Protocol Synchronize) flood, UDP (User Datagram Protocol) flood and ICMP (Internet Control Message Protocol) flood DDoS attacks are analyzed. Obtained results showed that DDoS attacks can be detected with high accuracy of 99.2%.
Citation Keybaskaya_ddos_2020