An Efficient Method for Detection of DDos Attacks on the Web Using Deep Learning Algorithms

Recently, DDoS attacks is the most significant threat in
network security. Both industry and academia are currently
debating how to detect and protect against DDoS attacks.
Many studies are provided to detect these types of attacks.
Deep learning techniques are the most suitable and efficient
algorithm for categorizing normal and attack data. Hence, a
deep neural network approach is proposed in this study to
mitigate DDoS attacks effectively. We used a deep learning
neural network to identify and classify traffic as benign or
one of four different DDoS attacks. We will concentrate on
four different DDoS types: Slowloris, Slowhttptest, DDoS
Hulk, and GoldenEye. The rest of the paper is organized as
follow: Firstly, we introduce the work, Section 2 defines the
related works, Section 3 presents the problem statement,
Section 4 describes the proposed methodology, Section 5
illustrate the results of the proposed methodology and shows
how the proposed methodology outperforms state-of-the-art
work and finally Section VI concludes the paper.