Cross-Site Scripting Attacks and Prevention Using Ml Algorithm and Blockchain
Year: 2024
Type: article
Abstract: Cross-Site Scripting (XSS) attacks pose a significant threat to web applications, allowing attackers to inject malicious scripts into otherwise benign and trusted websites. The impact of XSS attacks extends beyond simple security breaches, allowing malicious actors to steal confidential data, take control of legitimate user sessions, and orchestrate other serious security violations that put both individual privacy and application security at risk. While established security measures exist to combat XSS threats, these traditional approaches have become increasingly ineffective, primarily because they cannot adapt and respond to the constantly shifting landscape of attack strategies and emerging exploitation techniques. This project proposes a novel approach to detecting and preventing XSS attacks by integrating a machine learning (ML) algorithm with blockchain technology. The ML algorithm is trained on a comprehensive dataset of known XSS attack patterns and benign scripts, enabling it to accurately identify and block potential threats in real-time. Blockchain technology is employed to create a decentralized, tamper-proof ledger that records all detected threats and preventive actions, ensuring transparency and traceability in the security process. Integrating Machine Learning and blockchain creates an adaptive, scalable solution for preventing XSS attacks. ML identifies threats in real-time, while blockchain ensures secure, tamper-proof records. Together, they enhance security and accountability, offering a foundation for future cybersecurity advancements.
Author: Zaid Hamdhan, Ruppa Ranjit Raj, Riya Shanker, Ayush Raj
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6911849828
FWCI:
Cites:
Cited by:
Related to: