Detection of E-commerce Attacks and Anomalies using Adaptive Neuro-Fuzzy Inference System and Firefly Optimization Algorithm

Detection of attacks and anomalies is one of the new challenges in promoting e-commerce technologies. Detecting anomalies of a network and the process of detecting destructive activities in e-commerce can be executed by analyzing the behavior of network traffic. Data mining systems/techniques are used extensively in intrusion detection systems (IDS) in order to detect anomalies. Reducing the size/dimensions of features plays an important role in intrusion detection since detecting anomalies, which are features of network traffic with high dimensions, is a time-consuming process. Choosing suitable and accurate features influences the speed of the proposed task/work analysis, resulting in an improved speed of detection. The present papers utilize a neural network for deep learning to detect e-commerce attacks and anomalies of e-commerce systems. Overfitting is a common event in multi-layer neural networks. In this paper, features are reduced by the firefly algorithm (FA) to avoid this effect. Simulation results illustrate that a neural network system performs with high accuracy using feature reduction. Ultimately, the neural network structure is optimized by using particle swarm optimization (PSO) to increase the accuracy of attack detection capability.