Detection of Coronavirus Phishing Emails using Echo State Neural Network

  • omar younis Department of computer, College of Science, Garmian University, Kurdistan Region, Iraq
Keywords: Coronavirus, Phishing, email, ESN, Features, Legitimate.


E-mail is an important and fast  mean of conveying information among  people, banks, companies and organizations, that information is often important, sensitive and secret, this make it worthy to attackers who can use it for harmful purposes. Spread of coronavirus in most countries of the world and the huge amount of media coverage surrounding this virus led to emergence phishing emails by exploiting coronavirus pandemic. Phishing emails are scam messages used by fraudsters to take out secret information from persons by pretending that it is from official sources. In this paper a novel method has been proposed to detect the coronavirus phishing emails and distinguish them from legitimate mails by using Echo state neural network(ESN), after preprocessing the emails, features are selected from the header and body  of it, these features are given as fed to the (ESN) algorithm to classify emails as malicious or legitimate. The results showed the efficiency and accuracy of the algorithm used in the detection of coronavirus phishing emails, where the rate of accuracy, precision, recall and F-measure are 99.392, 98.892, 99888, and 99.387 respectively with low required processing time (0.00092 msec.) for testing and (165.19 msec.) for training.


Abdulfattah A. and Salih L. (2016). Speaker Recognition Using Discrete Wavelet Transform and Artificial Neural Networks. ZANCO Journal of Pure and Applied Sciences, 78-85.
Almomani A., B. B. Gupta, Atawneh S., M. A. and Almomani E. (2013). A Survey of Phishing Email Filtering Techniques, Ieee Communications Surveys & Tutorials, Vol. 15, No. 4, Fourth Quarter, 1-21.
Dai, Venayagamoorthy and Harley. (2009). an introduction to the echo state network and its applications in power system. International conference on intelligent system applications to power system, 1-7
Form L. N, Chiew K. L., and Tiong. (2015). email detection technique by using hybrid features. 9th International Conference on IT in Asia (CITA), 1-5.
Lallie H.S., Shepherd L.A., Nurse J. R (2020). Cyber security in the age of COVID-19: a timeline and analysis of cyber-crime and cyber-attacks during the pandemic, arXiv: 2006. 11929 v1 [cs.CR], 1-20.
Løvlid R.A. (2013). A novel method for training an echo state network with feedback error learning. Advances in Artificial Intelligence journal, 1-10.
Mohammed N. G, George L. E, (2013). Detection of phishing emails using feed forward neural network, International Journal of Computer Applications, 10- 16.
Moradpoor N., Clavie B.and Buchanan B. (2017). Employing machine learning techniques for detection and classification of phishing emails. Computing Conference, 1-8.
Montazer and Yarmohammadi. (2015). Detection of phishing attacks in Iranian e-banking using a fuzzy–rough hybrid system. Appl. Soft Computer, 482-492.
Nizamani S., Memon N., Glasdam M. and Nguyen D. D. (2014). Detection of fraudulent emails by employing advanced feature abundance. Egyptian Informatics Journal, 169-174.
Pandey M., Ravi V. (2012). Detecting phishing e-mails using Text and Data mining", IEEE International Conference on Computational Intelligence and Computing Research, 1-6.
Rathod S.B and Pattewar T.M. (2015). Content Based Spam Detection in Email using Bayesian Classifier. International Conference on Communications and Signal Processing (ICCSP), 1257-1261.
Rodan A., Tino P. (2016). Minimum complexity echo state network. IEEE Transactions on Neural Networks journal, 131-144.
Saeedd I. (2019). Artificial Neural Network Based on Optimal Operation of Economic Load Dispatch in Power System. ZANCO Journal of Pure and Applied Sciences, 94-102.
Sonmez, Y., Tuncer, T., Gokal, H., & Avci, E. (2018). Phishing web sites features classification based on extreme learning machine.6th International Conference on Digital Forensic and Security (ISDFS), 1-6.
Tuong and Peters. (2011). Model learning for robot control: a survey. Cognitive Processing journal, 319– 340.
Yasin A., Abuhasan A. (2016). An intelligent classification model for phishing email detection. International Journal of Network Security & Its Applications (IJNSA) Vol.8, No.4, 55-72.
Yang Z., Qiao C., Kan W. and Qiu J. (2019). Phishing email detection based on hybrid features", IOP Conf. Series: Earth and Environmental Science 252, 1-11.
How to Cite
younis, omar (2020) “Detection of Coronavirus Phishing Emails using Echo State Neural Network”, Zanco Journal of Pure and Applied Sciences, 32(5), pp. 78-85. doi: 10.21271/ZJPAS.32.5.7.
Mathematics ,Physics and Engineering Researches