Focusing on the Weakest Link: A Similarity Analysis on Phishing Campaigns Based on the ATT&CK Matrix

Table 3

Literature review on phishing techniques pertaining to the mail.

Focus	Study	Research design	Major findings
Mail	Peng et al. (2018)	Used datasets and library set such as accord.net to detect phishing mail sent to the organization in real-time environment.	The study presents phishing mail detection method using natural language processing and support vector machine. Features such as account of sender and receiver, IP address, subject and body of the e-mail, date, and time are analyzed to detect phishing mail.
	Gangavarapu et al. (2020)	Dataset consisted of 2,551 ham (legitimate) emails and 793 phishing emails, 500 spam emails collected from a variety of sources.	Presented study focused on detecting unsolicited bulk emails (UBEs) including spam emails and phishing emails. The study proposed technique for extraction and selection of the most discriminating e-mail contents and behavior-based feature set. Furthermore, it proposed detection model after comparative study using several state-of-the-art machine learning algorithms.