Detecting Malicious Websites by using Deep Q-Networks

Publication Date

1-1-2024

Document Type

Conference Proceeding

Publication Title

2024 Silicon Valley Cybersecurity Conference, SVCC 2024

DOI

10.1109/SVCC61185.2024.10637358

Abstract

Web applications are the most popular and critical applications for the Internet, but attackers also utilize web browsers for malicious purposes. Uniform Resource Loca- tor (URL) has been utilized to launch attacks on users, systems, and networks in different ways, such as phishing, defacement, malware, and spam. Identifying malicious URLs is the fundamental task to protect our digital assets. This paper presents a new malicious link detection system based on reinforcement learning to identify malicious URLs. It utilizes a Deep Q-Network (DQN) algorithm to make intelligent determinations by an agent in the designated environment. This paper analyzes malicious URL datasets with various feature sets with the DQN algorithm and provides a strategy to optimize the detection rates. The experimental results demonstrate the feasibility of preventing malicious URLs in different settings.

Funding Number

2244597

Funding Sponsor

National Science Foundation

Keywords

Deep Q-Network, Epsilon-greedy, malicious URLs, Reinforcement Learning

Department

Computer Engineering

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