Cybercriminals have weaponized metamorphic, polymorphic, and fileless malware to evade the entire traditional Microsoft ecosystem—from M365 endpoints to Azure Infrastructure-as-Code workloads. Static and dynamic detection alone are no longer sufficient for enterprise defense.

In this session, I present a heuristic-driven machine learning detection framework, originally developed through my academic research, used in live demonstrations, and delivered previously at industry events. The framework is specifically engineered to identify unknown and zero-day Trojan variants targeting Windows environments, leveraging Microsoft-aligned detection pipelines.

This deep-dive session will:

Deconstruct modern concealment techniques (packer evasion, obfuscation, anti-VM, anti-sandboxing, and adversarial ML poisoning).

Show why traditional AV, signature-based scanning, and sandbox environments fail against AI-generated Trojans (e.g., DeepLocker-style malware).

Demonstrate a full ML-powered heuristic architecture using realistic enterprise datasets (DLL imports, API call sequences, opcode n-grams, CFG flows, and hybrid feature engineering).

Compare the performance of XGBoost, SVM, Naïve Bayes, decision trees, random forest, and ensemble models under adversarial stress conditions.

Present a modular detection pipeline that can be integrated into Microsoft Sentinel, Defender for Endpoint, and Azure ML to enable real-time Trojan detection across enterprise systems.

Attendees will gain:

A production-ready detection blueprint tailored for Microsoft 365 and Azure workloads.

A deep understanding of how heuristic-based ML closes the gap left by static/dynamic techniques.

Insights into defending against adversarial attacks on ML models, a growing threat to enterprise AI systems.

Actionable architecture patterns that security engineers can deploy immediately in enterprise SOC environments.