Malicious Domain Detection using NLP Methods — A Review
Pradeepa Ganesan, R. Devi · 2022
Cyber security has emerged as a serious problem in today's business world. Attackers use different techniques to deceive the internet user to perform illegal activities such as stealing sensitive information, injecting malware, using the victim's system for further attack, etc. The majority of attacks are disseminated via social media posts, emails, and SMS messages. The message or webpage has embedded malicious links that will lead the user to the attacker's nest. Consequently, the issue of identifying malicious domain names has sparked significant concern. Researchers employ a variety of methods to find such malicious pages, including black or white lists, rules, machine or deep learning, visual similarity, and language processing (NLP). the attacker's employment of various tricks to confuse internet users, detecting or blocking an attack through a malicious domain is a difficult task. NLP with Machine Learning and NLP with Deep Learning methodologies receive the most interest from researchers due to their processing power. This paper provides an overview of several NLP text processing techniques as well as recent domain-related research.