Deep learning binary fruit fly algorithm for identifying SYN flood attack from TCP/IP. (2023)

• Record Type: Journal Article
• Authors: Nagaraju, Vankayalapati; Raaza, Arun; Rajendran, V.; Ravikumar, D.
• Is Part Of: Materials today. Volume 80:Part 3(2023)
• Page Start: 3086
• Page End: 3091
• Publication Date: 2023
• Subjects: DDoS attack -- Deep learning neural network -- Detection accuracy -- Synchronize (SYN) -- Fruit Fly Algorithm; Materials science -- Congresses -- Periodicals; 620.1
• DOI: 10.1016/j.matpr.2021.07.171
• Languages: English
• ISSNs: 2214-7853

Abstract

Abstract: SYN Flood Attack is one form of distributed denial of service attack that attains the handshake process of TCP. This attack consumes all available server resources and provokes legitimate traffic which aims to make the server unavailable. It causes serious damage to cloud server and networking protocols. The main objective of this research work is to train the neural network for detecting the attack and to secure network connection. A novel binary fruit fly optimization algorithm with deep learning is proposed to predict the syn flood attack. The proposed algorithm is implemented using the KDD cup dataset. DL- BFFA algorithm has achieved 99.96% detection accuracy for detecting the SYN Flood Attack. A comparison study is conducted to validate the proposed model.