Deep Reinforcement Learning for Detecting Malicious Websites

• Published in: arXiv.org 2019-05
• Main Authors: Chatterjee, Moitrayee, Namin, Akbar Siami
• Format: Article
• Language: English
• Subjects: Crime; Cybercrime; Fraud; Machine learning; Messages; Phishing; Telephone calls; Websites
• ISSN: 2331-8422

Phishing is the simplest form of cybercrime with the objective of baiting people into giving away delicate information such as individually recognizable data, banking and credit card details, or even credentials and passwords. This type of simple yet most effective cyber-attack is usually launched through emails, phone calls, or instant messages. The credential or private data stolen are then used to get access to critical records of the victims and can result in extensive fraud and monetary loss. Hence, sending malicious messages to victims is a stepping stone of the phishing procedure. A \textit{phisher} usually setups a deceptive website, where the victims are conned into entering credentials and sensitive information. It is therefore important to detect these types of malicious websites before causing any harmful damages to victims. Inspired by the evolving nature of the phishing websites, this paper introduces a novel approach based on deep reinforcement learning to model and detect malicious URLs. The proposed model is capable of adapting to the dynamic behavior of the phishing websites and thus learn the features associated with phishing website detection.