Study of statistical techniques and artificial intelligence methods in distributed denial of service (DDOS) assault and defense. Issue 1 (2nd January 2023)

• Record Type: Journal Article
• Authors: Suhag, Anil; Daniel, A.
• Is Part Of: Journal of cyber security technology. Volume 7:Issue 1(2023)
• Page Start: 21
• Page End: 51
• Publication Date: 2023-01-02
• Subjects: DDoS assault -- DDoS defense -- statistical (st) techniques -- artificial intelligence (ArIn) techniques; Computer security -- Periodicals; Data encryption (Computer science) -- Periodicals; 005.805
• DOI: 10.1080/23742917.2022.2135856
• Languages: English
• ISSNs: 2374-2917

Abstract

ABSTRACT: Distributed Denial of Service (DDoS) assaults have become a worldwide challenge and novel threat to the availability of the Internet services. This paper provides a complete and exhaustive understanding of different forms of DDoS assaults, as well as ways for identifying and detecting DDoS assaults and thereafter taking necessary measures for mitigation and prevention of DDoS assaults based on appropriate statistical (St) techniques and artificial intelligence (ArIn) techniques that are possible at the Open Source Interconnection (OpSoIn) layer. The soul of this paper is to facilitate guidance for improving DDoS defense methods, techniques and strategies, as well as incorporating effective, efficient and economical (E3) DDoS defense solutions. This study will enhance the scope of DDoS defense methods and provide a desirable direction to DDoS defense mechanism by highlighting types of DDoS assaults and thereafter, comparing the most common protection strategies against DDoS assaults and finally bringing out the existing research gaps. The review paper concludes that the DDoS defensive solutions that use statistical techniques and artificial intelligence approaches perform better against DDoS assaults.