Security has been the most widely researched topic, particularly within IoT, and has been considered as the major hurdle in the adoption of different applications of IoT. When it comes to IoV, security is considered as the most inevitable component to ensure a safe and smooth driving experience. CAV is the new era of transportation, integrating intelligence and self-driving capabilities within vehicles and that requires strong security measures to ensure safety. Security alone is not enough. Instead, a complete package including privacy of the vehicles and passengers needs to be added in addition to secure communication. This is because CAVs are under continuous cyber threats and attacks and the most important among them is the DDoS, where a remote attacker can hijack/launch attacks on vehicles remotely. Single point of failure attacks target the centralized trusted body in order to mislead the connected vehicles for personal gains. In this paper, the authors have proposed a secure communication system for CAVs using blockchain, which also ensures the privacy of the vehicle/people. The paper highlights the major components of the proposed system, and its performance is evaluated to check its efficiency against DDoS and Eclipse attacks. The unlinkability and anonymity of the vehicles have been ensured using the zk-SNAKR protocol over Blockchain.

Blockchain technology is an emerging decentralized and distributed technology that can maintain data security. It has the potential to transform many sectors completely. The core component of blockchain networks is the consensus algorithm because its efficiency, security, and scalability depend on it. A consensus problem is a difficult and significant task that must be considered carefully in a blockchain network. It has several practical applications such as distributed computing, load balancing, and blockchain transaction validation. Even though a lot of consensus algorithms have been proposed, the majority of them require many computational and communication resources. Similarly, they also suffer from high latency and low throughput. In this work, we proposed a new consensus algorithm for consortium blockchain for a leader selection using the reputation value of nodes and the voting process to ensure high performance. A security analysis is conducted to demonstrate the security of the proposed algorithm. The outcomes show that the proposed algorithm provides a strong defense against the network nodes’ abnormal behavior. The performance analysis is performed by using Hyperledger Fabric v2.1 and the results show that it performs better in terms of throughput, latency, CPU utilization, and communications costs than its rivals Trust-Varying Algo, FP-BFT, and Scalable and Trust-based algorithms.

Vehicle platooning enables vehicles to drive cooperatively on highways and motorways. This is to weigh numerous benefits such as lesser fuel consumption, safe drive and better road utilization. The main research focus of vehicle platoons is secure and dynamic platoon formation and ensuring privacy of vehicular data. Platoons are not safe from cyber-attacks and key is to safeguard the platoons from different known/unknown cyber threats. This paper introduces a dynamic and secure platoon formation technique targeting three different vehicle conditions on road. While the private credentials of CAVs is protected using zk-SNARK encryption protocol through permissioned Blockchain. The proposed system has been evaluated for its performance against DDoS and impersonation attack. Platoon formation time has been compared with other benchmarks and shows better results. While the performance against DDoS and impersonation attacks in comparison to the benchmarks is also improved. Since the limitation of zk-SNARK is it takes time to generate a proof and this has been also the focus of this study. The protocol has been fine tuned to adjust its parameters so that it could generate proof in less possible time.

Vehicular networks require privacy-preserving and secure authentication mechanisms to protect safety-critical communications against evolving cyber threats. Conventional authentication schemes relying on elliptic-curve cryptography (ECC) and RSA are vulnerable to quantum attacks, making them unsuitable for next-generation intelligent transportation systems. This paper proposes a unified post-quantum pseudonymous authentication and key establishment (UPQ-PAKE) scheme for secure vehicular networks. The proposed framework integrates dynamic pseudonymous identity construction, post-quantum digital signatures, and dual ephemeral key encapsulation into a single transcript-bound authenticated key exchange protocol. ML-DSA is employed for mutual authentication, while ML-KEM enables quantum-resistant session key establishment. Dynamic session-specific pseudonyms provide identity privacy and unlinkability. Furthermore, transcript binding, nonce freshness verification, and contributory dual-ephemeral session entropy strengthen the protocol against replay attacks, provide forward secrecy, and resist man-in-the-middle attacks under the quantum polynomial-time adversarial model. Formal security analysis demonstrates that the proposed scheme achieves authenticated key exchange security based on the IND-CCA security of ML-KEM and the EUF-CMA security of ML-DSA. Performance evaluation demonstrates that the proposed authentication technique maintains practical computational and communication overhead and provides post-quantum mutual authentication, dual-directional key establishment, and dynamic unlinkability, making it suitable for large-scale, real-time IoV deployments.

Domain generalization in computational histopathology is hindered by heterogeneity in whole slide images (WSIs), caused by variations in tissue preparation, staining, and imaging conditions across institutions. Unlike machine learning systems, pathologists rely on domain-invariant morphological cues such as nuclear atypia (enlargement, irregular contours, hyperchromasia, chromatin texture, spatial disorganization), structural atypia (abnormal architecture and gland formation), and overall morphological atypia that remain diagnostic across diverse settings. Motivated by this, we hypothesize that explicitly modeling biologically robust nuclear morphology and spatial organization will enable the learning of cancer representations that are resilient to domain shifts. We propose MorphGen (Morphology-Guided Generalization), a method that integrates histopathology images, augmentations, and nuclear segmentation masks within a supervised contrastive learning framework. By aligning latent representations of images and nuclear masks, MorphGen prioritizes diagnostic features such as nuclear and morphological atypia and spatial organization over staining artifacts and domain-specific features. To further enhance out-of-distribution robustness, we incorporate stochastic weight averaging (SWA), steering optimization toward flatter minima. Attention map analyses revealed that MorphGen primarily relies on nuclear morphology, cellular composition, and spatial cell organization within tumors or normal regions for final classification. Finally, we demonstrate resilience of the learned representations to image corruptions (such as staining artifacts) and adversarial attacks, showcasing not only OOD generalization but also addressing critical vulnerabilities in current deep learning systems for digital pathology. Code, datasets, and trained models are available at: https://github.com/hikmatkhan/MorphGen