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Detection of E-Mail Phishing Attacks – using Machine Learning and Deep Learning

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2022
Dhruv Rathee, Suman Mann

Dhruv Rathee and Suman Mann. Detection of E-Mail Phishing Attacks – using Machine Learning and Deep Learning. International Journal of Computer Applications 183(47):1-7, January 2022. BibTeX

	author = {Dhruv Rathee and Suman Mann},
	title = {Detection of E-Mail Phishing Attacks – using Machine Learning and Deep Learning},
	journal = {International Journal of Computer Applications},
	issue_date = {January 2022},
	volume = {183},
	number = {47},
	month = {Jan},
	year = {2022},
	issn = {0975-8887},
	pages = {1-7},
	numpages = {7},
	url = {},
	doi = {10.5120/ijca2022921868},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


Phishing is the most prominent cyber-crime that uses camouflaged e-mail as a weapon. In simple words, it is defined as the strategy adopted by fraudsters in-order-to get private details from persons by professing to be from well-known channels like offices, bank, or a government organization. In this era of modernization, electronic mails are accustomed globally as communiqué channel for both private and professional purposes. The particulars exchanged over e-mails are often confidential and sensitive for example info of bank statements, payment bills, debit-credit reports, and authentication data. This makes e-mails precious for hackers because they can exploit these details for maleficent intends. The main goal of the attackers is to acquire personal details by deceiving the e-mail recipient to click noxious link or download the attachment under false pretences. In the last few years, there is an exponential rise in cyber threats including the major ones, phishing e-mails have result in huge monetary and identity losses. Several models have been developed to separate ham and phished e-mails but attackers are always trying new methods to invade the privacy of the people. Hence, there is dire need to perpetually develop new models or to upgrade the existing ones. The focus of the paper is to elaborate that specifically centers around on both machine-learning (ML) and deep-learning (DL) approaches for detecting phishing e-mails. It shows comparative analysis and assessment of various DL and ML models that were proposed in the last few decades to classify phishing e-mails at different stages of crime in a systematic manner. This paper discusses the problem’s concept, its explication, and the anticipated future directions.


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Email Phishing, Phishing, Machine Learning, Deep Learning