Phishing Attacks in the Age of Embeddings: A Word-Vector Approach to Cyber Threats

The number of cyber threats such as phishing is steadily rising each year worldwide. Phishing attacks are a common type of cyber threats in which criminals pose as reliable organizations or trustworthy individuals in order to trick victims into revealing valuable information. Conventional detection techniques that rely purely on rule-based or heuristic methods struggle to keep up with the increasing number and complexity of phishing attacks. Developments in machine learning and natural language processing provide new ways to identify phishing attempts in order to overcome the difficulties in finding subtle patterns in the textual content of phishing messages. The aim of this paper is to examine whether by applying a machine learning technique known as word-vector representations to a phishing dataset it is possible to gain deeper comprehension about semantically similar words, and to increase understanding of potential phishing attempts. This is achieved by examining word embeddings in a continuous vector space model without the use of further resources, and only through an overview of the observed semantic similarities of the dataset's textual features. Experimental results confirm the potential of the word-vector approach to phishing detection and the benefits of understanding the inherent characteristics of phishing datasets.