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A technique for DoS attack detection in e-commerce transactions based on ECC and Optimized Support Vector Neural Network

Shaikh J. R., Iliev G.

Control and Cybernetics

2018

Vol. 47, no. 4

439--463

Cloud computing has become a significant area forbusiness due to the high demand from people engaging in commerce and hence, protecting the entities from attacks, like Denial-of-Service (DoS) attacks is essential. Therefore, an effective DoS attack detection technique is required in the e-commerce transactions to provide security in this space. Accordingly, in this paper, a technique is developed for DoS attack detection for the e commerce transactions by proposing Glowworm Swarm Optimization-based Support Vector Neural Network (GSO-SVNN) based authorization. The user and the server, who are registered for accessing the e-commerce web, are first registered and then, authenticated based on Elliptic Curve Cryptography (ECC) encryption with four verification levels follows. The proposed GSO-SVNN classifier, which is developed by incorporating the GSO algorithm in the training procedure of SVNN, determines the class of the user. The performance of the proposed technique is evaluated using four metrics, namely accuracy, precision, recall, and False Positive Rate (FPR), and the experimental results show that the maximum accuracy attained by the proposed DoS attack detection technique is 95.1%. This proves that the proposed technique is effective in detecting DoS attacks in e commerce applications using the proposed GSO-SVNN based authorization.