| Title | DDoS Attack Detection using Artificial Neural Network |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Dalvi, Jai, Sharma, Vyomesh, Shetty, Ruchika, Kulkarni, Sujata |
| Conference Name | 2021 International Conference on Industrial Electronics Research and Applications (ICIERA) |
| Keywords | artificial neural network, Artificial neural networks, Complexity theory, cybersecurity, DDoS attack detection, DDoS Attacks, denial-of-service attack, feature extraction, Human Behavior, machine learning, machine learning algorithms, Metrics, pubcrawl, resilience, Resiliency, Training, UDP-Flood |
| Abstract | Distributed denial of service (DDoS) attacks is one of the most evolving threats in the current Internet situation and yet there is no effective mechanism to curb it. In the field of DDoS attacks, as in all other areas of cybersecurity, attackers are increasingly using sophisticated methods. The work in this paper focuses on using Artificial Neural Network to detect various types of DDOS attacks(UDP-Flood, Smurf, HTTP-Flood and SiDDoS). We would be mainly focusing on the network and transport layer DDoS attacks. Additionally, the time and space complexity is also calculated to further improve the efficiency of the model implemented and overcome the limitations found in the research gap. The results obtained from our analysis on the dataset show that our proposed methods can better detect the DDoS attack. |
| DOI | 10.1109/ICIERA53202.2021.9726747 |
| Citation Key | dalvi_ddos_2021 |
DDoS Attack Detection using Artificial Neural Network