| Title | Classification Based Machine Learning for Detection of DDoS attack in Cloud Computing |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Mishra, Anupama, Gupta, B. B., Peraković, Dragan, Peñalvo, Francisco José García, Hsu, Ching-Hsien |
| Conference Name | 2021 IEEE International Conference on Consumer Electronics (ICCE) |
| Date Published | jan |
| Keywords | cloud computing, DDoS attack detection, denial-of-service attack, feature extraction, Human Behavior, Metrics, pubcrawl, reinforcement learning, resilience, Resiliency, Servers, supervised learning, Tools |
| Abstract | Distributed Denial of service attack(DDoS)is a network security attack and now the attackers intruded into almost every technology such as cloud computing, IoT, and edge computing to make themselves stronger. As per the behaviour of DDoS, all the available resources like memory, cpu or may be the entire network are consumed by the attacker in order to shutdown the victim`s machine or server. Though, the plenty of defensive mechanism are proposed, but they are not efficient as the attackers get themselves trained by the newly available automated attacking tools. Therefore, we proposed a classification based machine learning approach for detection of DDoS attack in cloud computing. With the help of three classification machine learning algorithms K Nearest Neighbor, Random Forest and Naive Bayes, the mechanism can detect a DDoS attack with the accuracy of 99.76%. |
| DOI | 10.1109/ICCE50685.2021.9427665 |
| Citation Key | mishra_classification_2021 |
Classification Based Machine Learning for Detection of DDoS attack in Cloud Computing