| Title | Using Deep learning for network traffic prediction to secure Software networks against DDoS attacks |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Sulaga, D Tulasi, Maag, Angelika, Seher, Indra, Elchouemi, Amr |
| Conference Name | 2021 6th International Conference on Innovative Technology in Intelligent System and Industrial Applications (CITISIA) |
| Date Published | nov |
| Keywords | composability, DDoS Attack, DDoS Attack Prevention, Deep Learning, denial-of-service attack, feature extraction, Human Behavior, Internet of Things, Memory management, Metrics, network traffic, pubcrawl, resilience, Resiliency, Software, software-defined network, Systems architecture, telecommunication traffic |
| Abstract | Deep learning (DL) is an emerging technology that is being used in many areas due to its effectiveness. One of its major applications is attack detection and prevention of backdoor attacks. Sampling-based measurement approaches in the software-defined network of an Internet of Things (IoT) network often result in low accuracy, high overhead, higher memory consumption, and low attack detection. This study aims to review and analyse papers on DL-based network prediction techniques against the problem of Distributed Denial of service attack (DDoS) in a secure software network. Techniques and approaches have been studied, that can effectively predict network traffic and detect DDoS attacks. Based on this review, major components are identified in each work from which an overall system architecture is suggested showing the basic processes needed. Major findings are that the DL is effective against DDoS attacks more than other state of the art approaches. |
| DOI | 10.1109/CITISIA53721.2021.9719978 |
| Citation Key | sulaga_using_2021 |
Using Deep learning for network traffic prediction to secure Software networks against DDoS attacks