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In today's technological world, distributed denial of service (DDoS) attacks threaten Internet users by flooding huge network traffic to make critical Internet services unavailable to genuine users. Therefore, design of DDoS attack detection system is on urge to mitigate these attacks for protecting the critical services. Nowadays, deep learning techniques are extensively used to detect these attacks. The existing deep feature learning approaches face the lacuna of designing an appropriate deep neural network structure for detection of DDoS attacks which leads to poor performance in terms of accuracy and false alarm. In this article, a tuned vector convolutional deep neural network (TVCDNN) is proposed by optimizing the structure and parameters of the deep neural network using binary and real cumulative incarnation (CuI), respectively. The CuI is a genetic-based optimization technique which optimizes the tuning process by providing values generated from best-fit parents. The TVCDNN is tested with publicly available benchmark network traffic datasets and compared with existing classifiers and optimization techniques. It is evident that the proposed optimization approach yields promising results compared to the existing optimization techniques. Further, the proposed approach achieves significant improvement in performance over the state-of-the-art attack detection systems.

neural computing and applications

Optimization of vector convolutional deep neural network using binary real cumulative incarnation for detection of distributed denial of service attacks