DATA-DRIVEN DEFENSE: ANALYZING DDOS ATTACKS WITH MULTIPLE DISCRIMINANT DATA ANALYSIS

Abstract

Ensuring security remains a paramount concern in both business and public spheres. Among the myriad threats faced, hacking attacks, particularly Distributed Denial of Service (DDoS) attacks at the application and network layers, loom large. Identified vulnerabilities often grant attackers unauthorized access, allowing them to compromise web services and impede network functionality.

The foundation of data security rests on three fundamental tenets: confidentiality, integrity, and availability. Confidentiality entails safeguarding data from illicit use, involving scrutiny, restricted sharing, and controlled dissemination. Information categorized as highly sensitive is deemed secret and necessitates stringent, exclusive protection. Integrity guarantees the unaltered veracity of data, affirming its origin and authenticity. Availability ensures data accessibility to authorized users, underpinning its utility and relevance.