Stacking ensemble approach for DDoS attack detection in software-defined cyber–physical systems. (April 2023)

• Record Type: Journal Article
• Authors: Mall, Ramya; Abhishek, Kumar; S., Manimurugan; Shankar, Achyut; Kumar, Abhay
• Is Part Of: Computers & electrical engineering. Volume 107(2023)
• Publication Date: 2023-04
• Subjects: DDoS -- SD-CPS -- Software defined networking -- Deep learning -- Genetic algorithm -- Stacking ensemble; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2023.108635
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: Distributed Denial of Service (DDoS) attacks are common and increasing in frequency. It renders the service inaccessible to legitimate users and degrades network performance. A complex structural environment called a Cyber–Physical System (CPS) is created by combining computation, connectivity, and physical parameters. Software-Defined Networking (SDN) is an emerging architecture that separates the data plane from the network plane. A central logic control resides in the control plane, making SDN vulnerable to DDoS attacks. SDN design ideas are broadened and applied to create software-defined Cyber–Physical Systems. Deep learning powers many artificial intelligence apps and services, enhancing automation by performing cognitive tasks without human intervention. It can perform feature extraction and classification on both small and large datasets. This paper presents a variety of Deep Learning models for efficiently detecting DDoS attacks in the SD-CPS framework through a scalable and adaptable SDN-based architecture. We could determine which Deep Learning techniques work best under various attack scenarios by examining multiple Deep Learning techniques. The Deep Learning models performed above 99% accuracy in classifying binary and multiclass data over unknown traffic when tested on two recent security datasets, the SDN-specific dataset and the CICDDoS2019 dataset. Graphical abstract: Highlights: The paper utilizes two datasets, one tailored to SDNs and the CICDDoS2019 dataset. Three distinct deep learning approaches identify binary and multiclass attacks. The binary accuracy is over 99% for the datasets, which is better than prior studies. Multiclass detects attack type with accuracy greater than 99±% for both datasets. The individual accuracy for type of attack ranges between 98%–99% in most cases.