# Security and Privacy for Modern Wireless Communication Systems, 2 nd Edition
## 2 Summary of the Contributions  
The first contribution, provided by [PERSON] et al., proposes a cooperative autonomous driving system tailored for urban environments by leveraging a V2X network-based framework. Unlike previous studies that primarily focus on highway scenarios, this work addresses the unique challenges of urban driving, such as managing traffic lights, pedestrian interactions, and dynamic vehicle clusters. A key innovation is the introduction of the Throttle-Steer-Brake (TSB) driving technique, which enables following vehicles to replicate the control commands of a leading vehicle, significantly reducing computational overhead and improving real-time responsiveness. The system also incorporates an HSV-based traffic light perception method, which outperforms AI-based models in speed and accuracy, and a pedestrian detection mechanism using V2P communication to trigger emergency braking when necessary. The system employs pseudonymized vehicle identifiers, AES encryption, and EdDSA digital signatures to ensure secure and reliable communication. Validated in the CARLA simulator, the proposed approach demonstrated approximately 466 times faster computation using TSB driving compared to waypoint-based methods and achieved 99.58% accuracy in traffic light recognition. The results confirm the feasibility of real-time, safe, and secure cooperative autonomous driving in urban settings. Future work will focus on deploying the system in real-world conditions, accounting for network latency and failures, and enhancing TSB driving in complex traffic scenarios. The second contribution, provided by [PERSON] et al., explores the enhancement of automotive intrusion detection systems (IDSs) by integrating CHERI (Capability Hardware Enhanced RISC Instructions), a hardware-enforced memory protection architecture. Motivated by the growing cybersecurity risks in connected vehicles, especially threats like IP spoofing and rule manipulation, the authors propose a CHERI-enabled IDS that isolates memory and safeguards detection rules from unauthorized access. The system enforces strict spatial and temporal memory safety by embedding capability-based access controls directly into the IDS design, effectively blocking advanced attacks. Experimental results show that the CHERI-enhanced IDS achieves 100% detection of spoofed packets and prevents all unauthorized rule modifications, even under real-time constraints. Implemented on the ARM Morello board, the solution operates within the latency thresholds required for automotive applications. Compared to traditional software-based IDS solutions, this hardware-centric approach ensures rule integrity, enhances detection robustness, and maintains system performance in resource-constrained environments. The authors conclude that CHERI-based IDS frameworks offer a scalable, secure foundation for future automotive cybersecurity systems, with potential for further optimization and integration with machine learning for anomaly detection. The third contribution, authored by [PERSON] et al., introduces LazyNTT, a novel approach to optimizing the Number Theoretic Transform (NTT), which is fundamental to lattice-based post-quantum cryptographic schemes. Traditional NTT relies heavily on Montgomery multiplication for modular reduction, which, while efficient, is still more costly than standard integer multiplication. To solve this problem, the authors propose two variants, SM-LazyNTT and SSM-LazyNTT, which strategically replace certain Montgomery multiplications with standard multiplications during intermediate stages of the NTT computation. These \"lazy\" replacements postpone modular reductions without affecting the final correctness, reducing computational overhead. SM-LazyNTT alternates between standard and Montgomery multiplications, while SSM-LazyNTT allows two consecutive standard multiplications before performing a Montgomery multiplication. The method is validated using parameters from Falcon and Kyber cryptosystems, showing performance improvements of up to 9% and 28%, respectively.