Development of an Intelligent Detection Framework for Trojan Horse Malware

Abstract

Amidst an escalating digital arms race, the burgeoning complexity of Trojan horse architecture has neutralized the efficacy of conventional signature-reliant defense paradigms. This research pioneers a high-fidelity Intelligent Detection Framework designed to transcend static identification by leveraging the predictive power of ensemble learning. Our experimental architecture utilized a curated corpus of 4,000 observations, maintaining a strict equilibrium between malicious Trojan payloads and benign system processes. The operational pipeline transformed raw telemetry into a refined feature space through a sequence of one-hot encoding, Min-max scaling, and rigorous Principal Component Analysis (PCA). By distilling the input data into the 20 most significant behavioral dimensions, the framework mitigated computational latency while amplifying signal clarity. Performance benchmarks revealed a stark divergence between the evaluated heuristics: while the Decision Tree (DT) model offered baseline competence, the Extreme Gradient Boosting (XGBoost) configuration attained a dominant 98.7% accuracy and a 99.2% recall. This near-absolute sensitivity is pivotal, as it virtually eliminates the "blind spots" typically exploited by zero-day mutations. By fusing behavioral telemetry with high-performance gradient boosting, this study establishes a scalable blueprint for fortifying endpoint security against the next generation of stealth-oriented cyber threats.