AI-Native Automotive Threat Intelligence
Clean-room automotive cybersecurity analyzer for synthetic UDS traces, security-state validation, negative-path testing, and engineering evidence export.
Connected-vehicle cybersecurity platform with threat fusion, attack simulation, digital twins, recovery, and security analytics.
Machine Learning-Based Anomaly Detection for Automotive CAN Network Traffic using Python.
Zero-Trust framework for automotive OTA firmware updates featuring Uptane metadata, DLT/DID verification, AIBOM tracking, and PQC benchmarks.
Research artifacts for Post-Quantum Security in Automotive Systems: hybrid ML-KEM-768 and ECDH-P384 IKEv2 evaluation.
A secure, backward-compatible SOME/IP reference-monitor proof of concept with state-aware access control and CMAC-authenticated oMAC footers.
VAHAKA is an open-source electric scooter cybersecurity simulator for practicing automotive security, ECU and CAN bus communication, threat modeling, and offensive/defensive security techniques in a safe, fully software-based environment.
Hands-on automotive cybersecurity lab for CAN, ECU simulation, UDS, and attack testing using Python and SocketCAN.
Real-time Two-Tiered Hybrid CAN Bus Anomaly Detection System for Electric Vehicles (ESP32 + ML).
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