Classification of the DDoS Attack over Flash Crowd with DNN using World Cup 1998 and CAIDA 2007 Datasets

Ch. Sekhar*, K. Venkata Rao **, M. H. M. Krishna Prasad ***
*,*** Department of Computer Science and Engineering, JNTU Kakinada, Andhra Pradesh, India.
** Department of Computer Science and Engineering, Vignan's IIT Kakinada, Andhra Pradesh, India.
Periodicity:January - March'2021
DOI : https://doi.org/10.26634/jse.15.3.18353

Abstract

Present day's e-commerce business has tremendously increased as everyone got Internet on their hands through their mobile devices. E-commerce big giants like Amazon, Alibaba, Flipkart, etc. have come up with surprise sales with huge discounts on the products called Flash Events (FE) or Flash Sales (FS). It attracts the customers to purchase the product on such specified dates. Huge client requests were coming into the servers on these days. Based on this scenario, attackers target these networks to degrade the performance of e-commerce portals by generating huge fake server requests called Distributed Denial of Service (DDoS) attacks. Network attacks caused during Flash Events (FE), Flash Sales (FS) are considered as Flash Crowd attacks (FC). With FC attacks, the performance of the server is reduced as well as it affects the clients by not sending proper responses. In this paper, the two datasets to CAIDA and WC 1998 datasets have been considered. WC 1998 dataset deals with flash crowd and CAIDA dataset have DDoS attack information. Similar features from both datasets have been taken and the flash crowd and DDoS attacks have been classified using the Deep Neural Network (DNN) approach. The accuracy of discriminating the DDoS and FC/FE with an accuracy of 70.49 % at 100 epochs and 72.1 % at 1000 epochs has been achieved.

Keywords

Flash Crowd, Flash Event, DDoS, Intrusion Detection, Deep Neural Networks.

How to Cite this Article?

Sekhar, C., Rao, K. V., and Prasad, M. H. M. K. (2021). Classification of the DDoS Attack over Flash Crowd with DNN using World Cup 1998 and CAIDA 2007 Datasets. i-manager's Journal on Software Engineering, 15(3), 29-36. https://doi.org/10.26634/jse.15.3.18353

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