Biblio

A classification issue in machine learning is the issue of spotting Distributed Denial of Service (DDos) attacks. A Denial of Service (DoS) assault is essentially a deliberate attack launched from a single source with the implied intent of rendering the target's application unavailable. Attackers typically aims to consume all available network bandwidth in order to accomplish this, which inhibits authorized users from accessing system resources and denies them access. DDoS assaults, in contrast to DoS attacks, include several sources being used by the attacker to launch an attack. At the network, transportation, presentation, and application layers of a 7-layer OSI architecture, DDoS attacks are most frequently observed. With the help of the most well-known standard dataset and multiple regression analysis, we have created a machine learning model in this work that can predict DDoS and bot assaults based on traffic.

DDoS attacks have grown in popularity as a tactic for potential hackers, cyber blackmailers, and cyberpunks. These attacks have the potential to put a person unconscious in a matter of seconds, resulting in severe economic losses. Despite the vast range of conventional mitigation techniques available today, DDoS assaults are still happening to grow in frequency, volume, and intensity. A new network paradigm is necessary to meet the requirements of today's tough security issues. We examine the available detection and mitigation of DDoS attacks techniques in depth. We classify solutions based on detection of DDoS attacks methodologies and define the prerequisites for a feasible solution. We present a novel methodology named D3 for detecting and mitigating DDoS attacks using cuckoo filter.