Web Vulnerability Detection

Authors: Lavanya V., Gayathri P., SaranyaVaishalini V.G.

Country: India

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Abstract: Malicious URLs pose a significant cybersecurity threat due to their potential to distribute malware, steal personal information, and launch phishing attacks. Conventional methods of detecting malicious URLs, such as blacklists and heuristics, are becoming less effective as attackers develop new evasion techniques. This study introduces a novel approach using Multilayer Perceptron (MLP) to quantify and predict the behavior of Malicious Web Services. This approach not only measures but also predicts the response time of these services, allowing for a quantitative ranking rather than a qualitative assessment. The proposed methodology aims to automatically select the most reliable Malicious Web Service by considering metrics like system predictability and response time variability. Through the use of real-world data and experiments, the researchers demonstrate the feasibility and usefulness of their approach.

Keywords: Machine Learning, Malicious URL Detection, Adversarial Attacks, Malicious Web Services.

Paper Id: 230427

Published On: 2024-01-09

Published In: Volume 12, Issue 1, January-February 2024

Cite This: Web Vulnerability Detection - Lavanya V., Gayathri P., SaranyaVaishalini V.G. - IJIRMPS Volume 12, Issue 1, January-February 2024.

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