Improving the performance of phishing attack detection systems based on synergy of neural network and Ali Baba and the forty thieves algorithm
Subject Areas : information technology
reza assareh
1
(Assistant Professor, Department of Computer Engineering, Yadgar Imam Khomeini Unit, Shahr Ray, Islamic Azad University, Shahr Ray, Iran.)
younes mobasheri
2
()
Keywords: cyber attacks, phishing attacks, machine learning, multilayer perceptron neural network, ali baba and the forty thieves ,
Abstract :
One of the cyber attacks is phishing attacks, which have increased rapidly in recent years. Defining robust, efficient and up-to-date methods for phishing detection is essential. Using machine learning to train a system that recognizes phishing messages is essential to increase the level of security against cyber attacks. By finding the weights and biases of the neural network through the algorithm of ali baba and the forty thieves, it is possible to identify phishing pages with high accuracy. Multi-layer perceptron neural network is used in the proposed method to classify and detect phishing attacks. The weights of the multi-layer perceptron neural network are found through the algorithm of ali baba and the forty thieves. The important thing is to choose the method with which the cost function is calculated, which includes 'mse', 'rmse' and 'accuracy'. The simulation of the proposed method has been done through matlab software. In the dataset, different features related to legal and phishing websites have been identified and 1353 different websites have been collected from different sources. The results of the proposed method are compared with the basic design in terms of precision, accuracy, F1_Score and AUC-ROC curve. According to the obtained results, the accuracy of the proposed method is 4.91% compared to the LR method, 5.7% compared to the support vector machine method, 3.72% compared to the K-nearest neighbor method, and 9.03% compared to the adaboost method. Compared to the multilayer perceptron method, it has improved by 3.53%, compared to the J48 method by 2.46%, and compared to the random forest method by 0.74%. Also, the proposed method has improved compared to the combined methods of meta-heuristic algorithms and neural network. The accuracy of the proposed method has improved by 1.3% compared to the EPO-ANN method and by 1.41% compared to the SSA-ANN method.
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