DeepDetect: Detection of Distributed Denial of Service Attacks Using Deep Learning. (3rd July 2019)

• Record Type: Journal Article
• Authors: Asad, Muhammad; Asim, Muhammad; Javed, Talha; Beg, Mirza O; Mujtaba, Hasan; Abbas, Sohail
• Is Part Of: Computer journal. Volume 63:Number 7(2020)
• Page Start: 983
• Page End: 994
• Publication Date: 2019-07-03
• Subjects: DDoS detection -- information security -- streaming data analysis -- deep learning models; Computers -- Periodicals; 005.1
• DOI: 10.1093/comjnl/bxz064
• Languages: English
• ISSNs: 0010-4620

Abstract

Abstract: At the advent of advanced wireless technology and contemporary computing paradigms, Distributed Denial of Service (DDoS) attacks on Web-based services have not only increased exponentially in number, but also in the degree of sophistication; hence the need for detecting these attacks within the ocean of communication packets is extremely important. DDoS attacks were initially projected toward the network and transport layers. Over the years, attackers have shifted their offensive strategies toward the application layer. The application layer attacks are potentially more detrimental and stealthier because of the attack traffic and the benign traffic flows being indistinguishable. The distributed nature of these attacks is difficult to combat as they may affect tangible computing resources apart from network bandwidth consumption. In addition, smart devices connected to the Internet can be infected and used as botnets to launch DDoS attacks. In this paper, we propose a novel deep neural network-based detection mechanism that uses feed-forward back-propagation for accurately discovering multiple application layer DDoS attacks. The proposed neural network architecture can identify and use the most relevant high level features of packet flows with an accuracy of 98% on the state-of-the-art dataset containing various forms of DDoS attacks.