Journal ID : TRKU-16-10-2020-11244
[This article belongs to Volume - 62, Issue - 10]
Total View : 345

Title : Reinforcement Modular Deep Learning Network To Detect And Curb The Distributed Denial of Service Attack

Abstract :

The Internet today, has proven and since become an effective means to share information. There is also the consequent proliferation of adversaries who aim at unauthorized access to information being shared over the Internet medium. Most of these adversaries employ various methods, tools and techniques that are well-crafted to coordinate such attacks – which aim to deny services to authorized users as well as degrade system performance and service quality. The Distributed denial of service attacks have become a major threat to the information society and age in that they are carefully crafted attacks of large magnitude that possess the capability to wreak havoc at very high levels and national infrastructures. This study posits intelligent systems via the use of machine learning frameworks to detect such. We employ reinforcement deep learning method to distinguish between benign exchange of data and malicious attacks from data traffic. Results shows consequent success in the employment of deep learning neural network to effectively differentiate between acceptable and non-acceptable data packets (intrusion) on a network data traffic. (9 pt).

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