trojan-detection

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

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

A benchmark for catching backdoors in neural networks — and finding out which detectors actually work. Implants known backdoors into a model zoo, runs published detectors against it blind, scores them all on identical metrics.

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

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

Autonomous AI assurance & cryptographic provenance engine - Trojan backdoor detection (STRIP), dataset outlier forensics, adversarial robustness, and Ed25519-signed verification certificates for safety-critical ML.

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.

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.

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

Distributed MLOps platform for generating, detecting, analyzing, and mitigating neural backdoors in deep-learning models.