Detect and Analyze Trojan attacks on deep neural networks that are designed to be difficult to detect.

Complete guide on how hackers inject Trojans into Mod APKs and how to protect your Android device from RATs, malware, and malicious apps. Includes threat analysis, Indicators of Compromise (IOCs), and cybersecurity best practices for mobile security. #android #cybersecurity #malware-analysis #ethicalhacking #mobile-security

Solution for the Trojan Detection Challenge (TDC2022 - https://trojandetection.ai) as part of NeurIPS 2022

EDR-style Trojan detection and response system with behavior tracking, risk scoring, human-in-the-loop decisioning, quarantine simulation, MITRE ATT&CK mapping, and incident reporting.

Investigating lightweight approaches for trojan detection in code models using only model parameters.

Enterprise-grade, distributed MLOps platform to simulate, detect, and mitigate Neural Trojans (backdoors) in DNNs—covering both offensive generation and defensive forensic audits with an end-to-end microservices architecture.

Some generic probabilistic methodologies to identify hardware trojans in arbitrary hardware designs

Detecting backdoored transformer models with an MNTD-style meta-network — team research project (PSC, École Polytechnique). 3,500+ trojaned GPT-style models generated, detector AUC 0.75.