A Logical Cyber Security Enabled Methodology Design for Identifying Distributed Denial of Service Attacks Using Enhanced Learning Principles

The Distributed Denial of Service (DDoS) attack is a common type of cyber-attack that targets computer networks. When a hostile user seeks to prohibit access to a service by overwhelming the system with data that is not intended, this is an example of the distributed denial of service attack, also known as a DDoS attack. Because of this, the provision of services to legitimate users is slowed down. DDoS attacks are launched when a large number of sources, such as a Botnet that controls the devices remotely for malicious reasons, simultaneously launch attacks on a target. DDoS refers to “distributed denial of service” attacks. In order to detect and prevent Distributed Denial of Service attacks, classification algorithms have been widely employed in research. Attacks using a Distributed Denial of Service may be easily carried out by using the weaknesses of networks as well as making application service demands. The relevance of this strategy cannot be overstated due to the fact that DDoS attacks are difficult to detect and prevent in real time and can have devastating effects. This study led to the development of a deep learning model called the Learning Model to Detect Cyber Attacks (LMDCA). Its purpose is to anticipate application layer DDoS attacks in real time. It is the responsibility of the availability component to guarantee that authorized users will always have access to the system. During the course of this study, we modeled both a denial-of-service attack as well as normal network traffic. Following that, we utilized packet-level analysis in an effort to differentiate between the aforementioned two usage patterns. Then, in order to detect and classify the various DOS attacks, we make use of the LMDCA-based classifier logic that was recommended.