A Cybersecurity - Driven Software Metrics Framework for Continuos Risk Quantification and Anomaly Detection

Modern systems running on software are in an increasingly hostile cyber environment where the traditional methods of risk assessment are not sufficient to provide continuous, context aware information of vulnerabilities and anomalous behavior. The root issue solved in this research is the lack of an integrated framework unifying the software engineering metrics and security telemetry to risk quantification for real-time use. This work is an attempt to develop a cybersecurity-driven software metrics framework to continuously measure, analyze, and predict security risk with the help of hybrid machine learning, graph-based dependency modeling, and attack path simulation. The proposed method combines static code indicators, runtime traces, threat intelligence and vulnerability scores into a multi-domain feature representation which is processed by supervised, unsupervised and graph neural models. Quantitative evaluation shows some huge improvements in comparison to top 5 baselines with an F1-score of 0.87, an AUC-ROC of 0.94, and reduces the mean time to detect incidents to 3.5 minutes. These results provide validation and proof of effectiveness for adoption of multi-modal feedback-based analytics pipeline for improving operational security posture. The study concludes that the combination of engineering metrics and security signals opens up the possibility of more accurate and pro-active risk management and to scale a path for the adoption of next generation DevSecOps automation.