World Journal of Engineering Research and Technology (WJERT) has indexed with various reputed international bodies like : Google Scholar , Index Copernicus , Indian Science Publications , SOCOLAR, China , International Institute of Organized Research (I2OR) , Cosmos Impact Factor , Research Bible, Fuchu, Tokyo. JAPAN , Scientific Indexing Services (SIS) , Jour Informatics (Under Process) , UDLedge Science Citation Index , International Impact Factor Services , International Scientific Indexing, UAE , International Society for Research Activity (ISRA) Journal Impact Factor (JIF) , International Innovative Journal Impact Factor (IIJIF) , Science Library Index, Dubai, United Arab Emirates , Scientific Journal Impact Factor (SJIF) , Science Library Index, Dubai, United Arab Emirates , Eurasian Scientific Journal Index (ESJI) , Global Impact Factor (0.342) , IFSIJ Measure of Journal Quality , Web of Science Group (Under Process) , Directory of Research Journals Indexing , Scholar Article Journal Index (SAJI) , International Scientific Indexing ( ISI ) , 

World Journal of Engineering
Research and Technology

( An ISO 9001:2015 Certified International Journal )

An International Peer Reviewed Journal for Engineering Research and Technology

ISSN 2454-695X

Impact Factor : 5.924

ICV : 79.45

News & Updation

  • Article Invited for Publication

    Article are invited for publication in WJERT Coming Issue

  • ICV

    WJERT Rank with Index Copernicus Value 79.45 due to high reputation at International Level

  • WJERT New Impact Factor

    Its our Pleasure to Inform you that WJERT Impact Factor has been increased from  5.549 to 5.924 due to high quality Publication at International Level


    JANUARY 2022 Issue has been successfully launched on 1 January 2022.

  • New Issue Published

    Its Our pleasure to inform you that, WJERT 1 January 2022 Issue has been Published, Kindly check it on




*Marwan H. Alsammarraie and Mohamad A. Alfayomi


Recently, the phishing attack is one of the critical threats against the Organizations, Internet users, service provider, cloud computing and many other fields in daily life. In the phishing attack, the intruder attempts to defraud the users and leak or steal the credential information, including personal information such as bank account, passwords etc., by sending a fooled email or SMS to redirect the user to an untrusted website. Various methods have been proposed in terms of filtering and detect different types of phishing attacks, however, the researchers and security information experts still studying to find a solution to assure the internet security from phishing and other attacks. Viewing SMS phishing messages are mostly short text and become a relatively low number associated with legitimate messages, new features for quick writing and oversampling technique for imbalanced data utilized to SMS phishing detection. In this research, a novel framework of the SMS phishing detection presented. The proposed method combines feature extraction, oversampling, optimization algorithm for feature selection and classification. The general framework for SMS phishing detection is consists of Data input, Data preprocessing, Feature extraction, Oversampling. Then, Feature optimization using binary Gray Wolf Optimizer Algorithm, Classification using support vector machine (SVM), and results and output.

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