AI-Driven Hybrid Framework for Detecting Outdated and Vulnerable Software Packages using SBOM and Anomaly Analysis

Mohan, K. Ananda; Rani, U. Mercy; Devi, M. Roopeswari; Srikanth, M.

doi:https://doi.org/10.55041/ijsmt.v2i3.383

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AI-DRIVEN HYBRID FRAMEWORK FOR DETECTING OUTDATED AND VULNERABLE SOFTWARE PACKAGES USING SBOM AND ANOMALY ANALYSIS

AUTHORS:

K. Ananda Mohan

U. Mercy Rani

M. Roopeswari Devi

M. Srikanth

Mentor

Affiliation

Department of CSE (Cyber Security), Bapatla Engineering College, Bapatla, Andhra Pradesh, India

DOI: 10.55041/ijsmt.v2i3.383

CC BY 4.0 License:

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Abstract

Outdated software packages are one of the primary entry points for cyber-attacks, as they often contain unpatched vulnerabilities that can be exploited by attackers. Despite the availability of vulnerability databases, many systems fail to continuously monitor and identify outdated dependencies across applications, operating systems, and web environments. This creates a significant security gap, especially in the context of zero-day and emerging threats.

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This article has undergone plagiarism screening and double-blind peer review. Editorial policies have been followed. Authors retain copyright under CC BY-NC 4.0 license. The research complies with ethical standards and institutional guidelines.

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