Modern Windows environments—managed through Intune, Microsoft Defender XDR, Windows 365, and Azure—continue to face rapidly evolving Trojan malware that bypasses traditional static and dynamic detection. In this deep technical session, I present a heuristic-based machine learning detection framework developed through my postgraduate research, engineered specifically for Microsoft enterprise security stacks.

This session delivers a 300–400 level deep dive into how heuristic features (API call sequences, opcode patterns, entropy scoring, DLL imports, PE header anomalies, and behavioral telemetry from Windows internals) can be combined with ML models such as XGBoost, SVM, Random Forest, and ensemble classifiers to identify advanced Trojans—including polymorphic, metamorphic, and adversarial AI-generated variants.

We will walk through a production-ready architecture showing how these ML models can be integrated with:

Microsoft Defender for Endpoint (custom threat indicators, behavioural rules, ASR mappings)

Microsoft Sentinel (UEBA correlation, analytics rules enriched with ML anomaly scores)

Azure Machine Learning (model training, deployment pipelines, real-time inference)

Windows 11 / Windows Server (ETW-based telemetry collection and feature extraction)

The session includes a live demonstration of the detection pipeline running against real Windows samples inside an isolated VM, showing feature extraction, ML scoring, and automated alerting into Defender or Sentinel.

Attendees will gain a practical blueprint for deploying next-generation malware detection capabilities across hybrid and cloud-native workplaces, MSP-managed environments, and enterprise Microsoft security ecosystems. This session is grounded in real research, real experiments, and real-world implementation scenarios.