An Enhanced Mechanism To Detect Drdos Attacks On Dns Using Adaptive Thresholding Technique - Al Ogaili, Riyadh Rahef Nuiaa (2023)
Demand for cyberspace-enabled services has expanded dramatically in recent years, in lockstep with the global Internet user population expansion. This rising demand for these services has increased the number of cyber threats launched by attackers, as well as the diversity and sophistication of the attack strategies used to target those services. By exploiting DNS flaws, cyber attackers conduct a Distributed Reflection Denial of Service (DRDoS) attack. As a result, these types of attacks exploit the method, functionality, and operation of open DNS resolvers to compromise the DNS. Additionally, to intensify the attack by boosting the attack bandwidth to overwhelm the victim with a vast number of DNS answers. As a result, traditional mechanisms are incapable of detecting these types of cyberattacks. As a result, existing detection mechanisms are unable to detect these forms of cyber intrusions. Thus, this thesis presents a mechanism for detecting DRDoS attacks on DNS that is strengthened by the use of modified metaheuristic algorithms and adaptive thresholding techniques based on machine learning algorithms (EMDDMAT).