Vehicular ad hoc networks (VANETs) require authentication systems that balance privacy, scalability, and post-quantum security. While lattice-based V-LDAA offers quantum resistance, it faces challenges in signature size, traceability, and integration. We propose post-quantum traceable direct anonymous attestation (PQ-TDAA), combining National Institute of Standards and Technology (NIST)-standard Dilithium2 and Falcon-512 signatures with adapted Beullens-style blind signatures and Fiat–Shamir simplified Schnorr proofs, reducing proof size by 69.2% (8 kB vs. V-LDAA’s 26 kB) and supporting European Telecommunications Standards Institute Technical Specification (ETSI TS) 102 941-compliant traceability through Road Side Unit (RSU)-assisted verification. Evaluated using SageMath, Python 3.11, and NS-3, PQ-TDAA-Falcon-512 achieves 8.1 ms and 49.7 ms end-to-end delays at 10 and 20 vehicles, respectively, with 64.7 Mbps goodput on congested 802.11p channels, showing promise for densities of ≤50 vehicles and advantages over Dilithium2. Real-world validation on ARM Cortex-A76 (Raspberry Pi 5, emulating automotive OBUs) yields sub-0.5 ms V2V cycles within 100 ms beacon intervals, supporting practical embedded deployment. Future work will extend PQ-TDAA to emerging 5G and NR-V2X settings, integrate more realistic mobility and channel models through coupled NS-3 and SUMO co-simulation, and investigate side-channel resistance for enhanced scalability and robustness in real deployments.

The application of Information and Communication Technology (ICT) has had an enormous impact on our various aspects of life. On the other hand, the rising problem is the increase of complex cyber incidents, i.e., trojan malware incidents. Trojan malware incidents that do not carry out immediate response can significantly impact the system services. Besides, it requires an appropriate tool to analyze the incidents. In this paper, we observe the capability of the GRR tool collaborated with Plaso and Timesketch as our advanced analytical tool to help locate the incident’s Indicator of Compromise (IoC) on the system affected by the trojan. We used 30 Hawkeye trojans as the sample in the assessment to perform an in-depth analysis. We aim to measure the performance of the tool in terms of accuracy and time consumed. The result shows that the GPT tool has high accuracy in finding the IoC parameter in a comparable time. Furthermore, our tool can parse the output data and display the result in a timeline visualization.