| Title | A Unique Deep Intrusion Detection Approach (UDIDA) for Detecting the Complex Attacks |
| Publication Type | Conference Paper |
| Year of Publication | 2022 |
| Authors | Krishna, P. Vamsi, Matta, Venkata Durga Rao |
| Conference Name | 2022 International Conference on Edge Computing and Applications (ICECAA) |
| Keywords | computer networks, Deep Learning, Intrusion detection, Intrusion Detection System (IDS), Network security, Protocols, pubcrawl, Radio frequency, resilience, Resiliency, Scalability, Security by Default, Sensitivity, software-defined network (SDN), Support vector machines, virtualization |
| Abstract | Intrusion Detection System (IDS) is one of the applications to detect intrusions in the network. IDS aims to detect any malicious activities that protect the computer networks from unknown persons or users called attackers. Network security is one of the significant tasks that should provide secure data transfer. Virtualization of networks becomes more complex for IoT technology. Deep Learning (DL) is most widely used by many networks to detect the complex patterns. This is very suitable approaches for detecting the malicious nodes or attacks. Software-Defined Network (SDN) is the default virtualization computer network. Attackers are developing new technology to attack the networks. Many authors are trying to develop new technologies to attack the networks. To overcome these attacks new protocols are required to prevent these attacks. In this paper, a unique deep intrusion detection approach (UDIDA) is developed to detect the attacks in SDN. Performance shows that the proposed approach is achieved more accuracy than existing approaches. |
| DOI | 10.1109/ICECAA55415.2022.9936156 |
| Citation Key | krishna_unique_2022 |
A Unique Deep Intrusion Detection Approach (UDIDA) for Detecting the Complex Attacks