Today, considering technology development, increased use of Internet in businesses, and movement of business types from physical to virtual and internet, attacks and anomalies have also changed from physical to virtual. That is, instead of thieving a store or market, th
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Today, considering technology development, increased use of Internet in businesses, and movement of business types from physical to virtual and internet, attacks and anomalies have also changed from physical to virtual. That is, instead of thieving a store or market, the individuals intrude the websites and virtual markets through cyberattacks and disrupt them. 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. In this article, by using data mining algorithms such as Bayesian, Multilayer Perceptron, CFS, Best First, J48 and PSO, we were able to increase the accuracy of detecting anomalies and attacks to 0.996 and the error rate to 0.004.
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