Robust Email Phishing Detection using Machine Learning and Deep Learning Approach

Hajar Fares; Nouhayla Mouakkal; Youssef Baddi; Nirmin Hajraoui

Author(s)

Hajar Fares Faculty of Science, Abdelmalek Essaadi University, Tetouan, Morocco

Nouhayla Mouakkal Mathematics and Computer Science. ENSET, Hassan II University, Mohammedia, Morocco

Youssef Baddi ESTSB, Chouaib Doukkali University, El Jadida, Morocco

Nirmin Hajraoui ENSA, University of Abdelmalek Essaadi, Tetouan, Morocco

Abstract

Phishing Email is a type of attack that can have serious consequences, despite the advances research on the field of security. Recently the number of Phishing attacks have been increased because of Internet widespread in various daily life applications. Phishing attack have various types, which make the task of detection, using the classical solutions of security no longer practical. According to many researches, approaches based on artificial intelligence, specifically on learning modeles have proven to be an effecient solution of cyber Security. This study paper's objective is to propose an efficient and accurate method for enhancing phishing emails detection, based on learning model and features selection technique such as Term Frequency-Inverse Document Frequency (TF-IDF) vectorization to extract only the significant features. For that reason, different computational models were developed: LSTM, Random Forest, SVM and three Naive Bayes variants (ComplementNB, BernoulliNB, and MultinomialNB) and tested rigorously using a specialized dataset. The results obtained from our experiment, revealed that the SVM model achieved the highest scores in all validation tests, with an accuracy of 0,971208, followed by the BernoulliNB with an accuracy of 0.967218. This demonstrates that our approach can enhance the accuracy rate with a minimum time of execution and that our approach is robust and reliable in terms of email phishing detection.

Publication Details

Page(s) 1-108

DOI DOI not available

Published Journal: International Journal of Communication Networks and Information Security, Volume: 16, Issue: 3, Year: 2024

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Keywords

Machine learning, Deep learning, Malware Detection, Cyber-security, Phishing Attacks

References

Kamarudin S. N. A.,Hamid I. R. A.,C. F. M. Foozy Z.,Abdullah Z. .2022 .Feature selection approach to detect phishing website using machine learning algorithm. in AIP Conference Proceedings, 2644 : .

Yeboah-Boateng E. O.,Amanor P. M. .2014 .Phishing, smishing & vishing: An assessment of threats against mobile devices. ” Journal of Emerging Trends in Computing and Information Sciences, 5(4) : 297-307.

Ulfath R. E.,Sarker I. H.,M. J. M. Chowdhury M.,Hammoudeh M. .2022 .Detecting smishing attacks using feature extraction and classification techniques,”. in Proceedings of the International Conference on Big Data, IoT, and Machine Learning: BIM 2021, : 677-689.

Buch R.,Ganda D.,Kalola P.,Borad N. .2017 .World of cyber security and cybercrime,” Recent Trends in Programming Languages. , 4(2) : 18-23.

Alqwifli H.,Selmi A. .2023 .Hybrid intrusion detection model for enhancing the security and reducing the computational cost. International Journal of Communication Networks and Information Security (IJCNIS), 15(2) : 6188-142.

Atawneh S.,Aljehani H. .2023 .Phishing email detection model using deep learning. ” Electronics, 12(20) : 4261.

Gupta Brij B. .2020 ."Phishing attack detection using a search engine and heuristics-based technique. " Journal of Information Technology Research (JITR) 13, (2) : 94-109.

Arivukarasi M.,Antonidoss A. .2019 .Artificial intelligence techniques for phishing detection. International Journal of Innovative Technology and Exploring Engineering (IJITEE), 8(11) : .

Saravanan P.,Subramanian S. .2020 .A framework for detecting phishing websites using GA based feature selection and ARTMAP based website classification,” Procedia Computer Science. , 171 : 1083-1092.

Alsariera Y. A.,Adeyemo V. E.,Balogun A. O.,Alazzawi A. K. .2020 .AI meta-learners and extra-trees algorithm for the detection of phishing websites. ” IEEE Access, 8 : 142532-142542.

Ennaji A.,Sabri M. A.,Aarab A. .2024 .Ensemble learning with weighted voting classifier for melanoma diagnosis. ” Multimedia Tools and Applications, : 1-17.

Mienye I. D.,Sun Y. .2022 .A survey of ensemble learning: Concepts, algorithms. ” IEEE Access, 10 : 99129-99149.

Abayomi-Alli O. O.,Damaševičius R.,Maskeliūnas R.,Misra S. .2022 .An ensemble learning model for COVID19 detection from blood test samples. ” Sensors, 22(6) : 2224.

Alotaibi Y.,Ilyas M. .2023 .Ensemble-learning framework for intrusion detection to enhance internet of things' devices security. ” Sensors, 23(12) : 5568.

Abdulraheem R.,Odeh A.,and I. Keshta A. .2022 .Efficient email phishing detection using machine learning. ” in 2022 IEEE 12th Annual Computing and Communication Workshop and Conference (CCWC), : 0354-0358.

Ubing A. A.,K. B. Jasmi S.,Abdullah A.,Jhanjhi N. Z.,Supramaniam M. .2019 .Phishing website detection: An improved accuracy through feature selection and ensemble learning. International Journal of Advanced Computer Science and Applications, 10(1) : .

Bouijij H.,Berqia A. .2021 .Machine learning algorithms evaluation for phishing URLs classification,” in 2021 4th International Symposium on Advanced Electrical and Communication Technologies (ISAECT), IEEE. , : 01-05.

Yerima S. Y.,Alzaylaee M. K. .2020 .High accuracy phishing detection based on convolutional neural networks. ” in 2020 3rd International Conference on Computer Applications & Information Security (ICCAIS), : 1-6.

Bouijij H.,Berqia A.,Saliah-Hassan H. .2022 .“Phishing URL classification using extra-tree and DNN,” in 2022 10th International Symposium on Digital Forensics and Security (ISDFS), IEEE. , : 1-6.

Chen J.,Guo C. .2006 .Online detection and prevention of phishing attacks. , : 1-7.

Subasi A.,Molah E.,Almkallawi F.,Chaudhery T. J. .2017 .Computing Technologies and Applications (ICECTA), IEEE. , : 1-5.

Alam M. N.,Sarma D.,Lima F. F.,I. Saha F. F.,Hossain S. .2020 .Phishing attacks detection using machine learning approach. 2020 Third International Conference on Smart Systems and Inventive Technology (ICSSIT), : 1173-1179.

Hossain S.,Sarma D.,Chakma R.J. .2020 .Machine learning-based phishing attack detection. International Journal of Advanced Computer Science and Applications, 11(9) : .

Shaukat M. W.,Amin R.,M. M. A. Muslam A. H.,Alshehri J.,Xie J. .2023 .A hybrid approach for alluring ads phishing attack detection using machine learning. ” Sensors, 23(19) : 8070.

Abedin N. F.,Bawm R.,Sarwar T.,Saifuddin M.,Rahman M. A.,Hossain S. .2020 .Phishing attack detection using machine learning classification techniques. ” in 2020 3rd International Conference on Intelligent Sustainable Systems (ICISS), : 1125-1130.

Cop C. .2024 .. Accessed: 10 jully, : .

Musmuharam M.,Suharjito S. .2023 .Detection of distributed denial of service attacks in software defined networks by using machine learning”. International Journal of Communication Networks and Information Security (IJCNIS), 6214(3) : 13-25.

Staudemeyer R. C.,Morris E. R.,Understanding E. R. .2019 .-A tutorial into long short-term memory recurrent neural networks. , : .

Arya N. .2024 .ai-machine-learning-and-deep-learning/random-forest-in-machinelearning/. ” Ejable. Accessed: 10 jully, : .

Xu S. .2018 .Bayesian Naïve Bayes classifiers to text classification. ” Journal of Information Science, 44(1) : 48-59.

Raizada R. D.,Lee Y. S. .2013 .Smoothness without smoothing: Why Gaussian naive bayes is not naive for multisubject searchlight studies,” PloS One. , 8(7) : e69566.

Rao R. S. .2015 .Phishshield: A desktop application to detect phishing webpages through heuristic approach. , 54 : 147-156.

Jeeva S. C.,Rajsingh E. B. .2016 .Intelligent phishing url detection using association rule mining. ” Humancentric Computing and Information Sciences, 6 : 1-19.

Bin S.,Qiaoyan W. .2010 .A DNS based anti-phishing approach. Wireless Communications and Trusted Computing, 2 : 262-265.

Staudemeyer R. C.,Morris E. R.,Understanding E. R. .2019 .-a tutorial into long short-term memory recurrent neural networks. , : .

Umar N.,Nur M.A. .2022 .Application of naïve bayes algorithm variations on Indonesian general analysis dataset for sentiment analysis,” Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi). , 6(4) : 585-590.