Evaluating Security Effectiveness in Knowledge-Based Expert Systems Using Risk Metrics

Priyanka Mohan Shelar; Babasaheb Jotiram Mohite

doi:10.36948/ijfmr.2025.v07i05.58234

Evaluating Security Effectiveness in Knowledge-Based Expert Systems Using Risk Metrics

Author(s)	Ms. Priyanka Mohan Shelar, Dr. Babasaheb Jotiram Mohite
Country	India
Abstract	Knowledge-Based Expert Systems (KBES) are essential elements in intelligent decision-making applications; however, their rule-based architectures render them vulnerable to threats such as unauthorized inference manipulation and knowledge corruption. This paper introduces a quantitative framework for assessing the security efficacy of Knowledge-Based Expert Systems (KBES) through risk metrics derived from a Threat–Vulnerability–Control (TVC) model. The research presents and utilizes three security indices—Security Effectiveness Index (SEI), Exploit Threat Index (ETI), and Attack Success Rate (ASR)—to evaluate and measure system resilience. Experimental evaluation shows that using structured controls like access management, rule validation, and source authentication makes systems much safer. The SEI goes up by 42%, while the ETI and ASR go down by 37% and 46%, respectively. The findings validate that risk-based quantitative assessment provides a more objective measure of KBES security performance, contributing a replicable model for intelligent system protection.
Keywords	Knowledge-Based Expert Systems (KBES),Security Effectiveness, Risk Metrics,Threat–Vulnerability–Control Model, Information Security Evaluation, Inference Engine Protection, Rule-Based Systems,Security Assessment Methodology, Knowledge Integrity and Assurance
Field	Computer Applications
Published In	Volume 7, Issue 5, September-October 2025
Published On	2025-10-18
DOI	https://doi.org/10.36948/ijfmr.2025.v07i05.58234

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Evaluating Security Effectiveness in Knowledge-Based Expert Systems Using Risk Metrics

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