文章基本信息

标题：NEURAL NETWORK WITH PSO BASED TRAINING TO DETECT SPOOFING WEBSITES
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作者：SOZAN ABDULLAH MAHMOOD ; NOOR GHAZI M. JAMEEL ; SHAIDA JUMA SAYDA 等
期刊名称：Journal of Theoretical and Applied Information Technology
印刷版ISSN：1992-8645
电子版ISSN：1817-3195
出版年度：2018
卷号：96
期号：12
出版社：Journal of Theoretical and Applied
摘要：With the advent of Internet, various online attacks were increased among them and the most well-known is a spoofing attack. Web spoofing is a type of spoofing in which fake and spoofing websites made by means of fraudsters to duplicate real websites. Spoofing websites represent legitimate websites which attract users into visiting fake websites to steal users sensitive, personal information or install malwares in their devices. The scammers will use the stolen information for illegal purposes. The specific intention of this paper is to build a new intelligent system that detects and recognize between trusted and spoofing websites which try to mimic the trusted sites because it is very difficult to visually recognize whether they are spoofing or legitimate. This paper deals with the detection of spoofing websites using Neural Network (NN) trained with Particle Swarm Optimization (PSO) algorithm. An Information gain algorithm is used for feature selection, which was a useful step to remove the unnecessary features and reduce time. The Information gain seems to improve the classification accuracy via reducing the number of extracted features and used as an input for training the NN using PSO. Training neural network using PSO provides less training time and high accuracy which achieved 99.18% compared to NN trained with back propagation algorithm which takes more time for training and less accuracy which was 98.20%. The proposed technique is evaluated with a dataset of 2500 spoofing sites and 2500 legitimate sites. The results show that the technique can detect over 99.18% spoofing sites with NN trained using PSO.
关键词：Web Spoofing; Information Gain; Neural Network; Particle Swarm Optimization