Contextfuse: Advanced Container Security with Contextual Intelligence

Container security became a particularly key problem with the widespread use of containerized architecture in organizations. Current ap-‎approaches typically focus on isolated security dimensions, creating gaps in detection and leading to both false positives and false negatives. ‎This paper introduces ContextFuse, an integrated container security system that combines vulnerability assessment, behavioral analysis, and ‎contextual intelligence to provide comprehensive security evaluation. ContextFuse implements a novel weighted consensus algorithm for ‎vulnerability assessment, applies transfer learning for behavioral analysis, and uses a graph-based approach for modeling security relationships, while incorporating an adaptive learning framework that continuously improves based on feedback. Our evaluation using a dataset of ‎‎1,000 containers demonstrates significant improvements over existing security tools, with 51.8% higher accuracy, 4.2% higher precision, ‎and 18.0% higher recall than baseline approaches. The system successfully identified 85% of simulated attacks with a false positive rate of ‎only 10%, and improved security assessment accuracy from 70% to 85% after processing just 10 feedback instances. ContextFuse effectively identifies complex security risks that would be missed by conventional tools while providing explainable security scores and actionable recommendations, demonstrating that an integrated, context-aware approach can significantly improve container security practices‎.