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AI-Generated Malware Detection in Web Applications De-Obfuscation and Analysis

Author(s) Sandeep Phanireddy
Country United States
Abstract Web applications face evolving threats as attackers employ advanced techniques including AI driven malware and code obfuscation to infiltrate servers and client interfaces. Malicious scripts can hide behind seemingly benign code, making conventional signature-based methods insufficient. This paper discusses how artificial intelligence (AI) models identify and de-obfuscate malicious code in modern web platforms. We explore established and emerging techniques, covering static and dynamic analysis, advanced machine learning (ML) classification, sample formulas for detection thresholds, and recommended architecture. By highlighting practical tools and frameworks, this paper offers a roadmap for developers and security teams aiming to protect critical web assets from stealthy malware attacks.
Keywords Malware Detection, AI, De-obfuscation, Web Application Security, Machine Learning, Code Analysis, Obfuscated Scripts
Field Engineering
Published In Volume 1, Issue 4, December 2020
Published On 2020-12-08
Cite This AI-Generated Malware Detection in Web Applications De-Obfuscation and Analysis - Sandeep Phanireddy - IJLRP Volume 1, Issue 4, December 2020. DOI 10.5281/zenodo.14960419
DOI https://doi.org/10.5281/zenodo.14960419
Short DOI https://doi.org/g86v5b
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