Applying swarm intelligence and data mining approach in detecting online and digital theft. (17th October 2022)

• Record Type: Journal Article
• Authors: Bejandi, Saba Malekpour; Taghva, Mohammad Reza; Hanafizadeh, Payam
• Is Part Of: International journal of information and computer security. Volume 19:Number 1/2(2022)
• Page Start: 142
• Page End: 167
• Publication Date: 2022-10-17
• Subjects: phishing -- fake web pages -- swarm intelligence -- feature selection; Computer security -- Periodicals; Information systems management -- Security measures -- Periodicals; Computer networks -- Security measures -- Periodicals; Information technology -- Security measures -- Periodicals; 005.805
• Languages: English
• ISSNs: 1744-1765

Abstract

Various methods have been proposed to deal with phishing attacks. Using machine learning along with data mining, such as MLP techniques, is one of the practical approaches to detect these attacks. To detect phishing attacks by the neural network with proper accuracy, it is necessary to intelligently do feature selection. In this research, the emperor penguin optimiser algorithm has been used as feature selection in detecting phishing attacks by a MLP. Experiments show that the error of the proposed method for detecting phishing is less than those of WOA, BOA, and SHO algorithms. The results show that the population increase in the proposed method reduces the value of the feature selection function and phishing detection error by about 69.57% and 24.56%, respectively. The RMSE error in detecting phishing attacks in the proposed method occurred to a lesser degree in comparison with MLP, DT, SVM, and BN. The accuracy, sensitivity, and precision of the proposed method in detecting phishing attacks are 98.12%, 97.92%, and 97.88%, respectively. The proposed method is more accurate in detecting phishing attacks than methods such as GA and PSO algorithms and is more accurate than BPNN, SVM, NB, C4.5, RF, and kNN.