Abstract Artificial intelligence (AI) techniques implemented at a large scale in intelligent transport systems (ITS), have considerably enhanced the vehicles' autonomous behaviour in making independent decisions about cyber threats, attacks, and faults. While, AI techniques are based on data sharing among the vehicles, it is important to note that sensitive data cannot be shared. Thus, federated learning (FL) has been implemented to protect privacy in vehicles. On the other hand, the integrity of data and the safety of aggregation are ensured by using blockchain technology. This paper applied classification approaches to VANET and ITS cyber‐threats detection at the vehicle. Subsequently, by using blockchain and by applying an aggregation strategy to different models, models from the previous step were uploaded in a smart contract. Lastly, we returned the updated models to the vehicles. Furthermore, we conducted an experimental study to measure the effectiveness of the proposed prototype. In this paper, the VeReMi data set was distributed in a balanced manner into five parts in the experimental study. Thus, classification techniques were executed by each vehicle separately, and models were generated. Upon the aggregation of the models in blockchain, they were returned to the vehicles. Lastly, the vehicles updated their decision functions and accessed the precision and accuracy of cyber‐threat detection. The results indicated that the precision and accuracy decreased by 7.1% on average with comparable F1‐score and recall. Our solution ensures the privacy preservation of vehicles whereas blockchain guarantees the safety of aggregation technique and low gas consumption.

In this paper, we deal with one of the major issues occurring in the integration of Cloud Computing (CC) with the Internet of Things (IoT), known as (CoT), i.e., the communication of unnecessary data that consumes bandwidth, much power, processing time and storage space. Dimensionality reduction presents a pre-processing phase used to remove redundant features and noisy, unnecessary and irrelevant data. Dimensionality reduction methods have been proposed, and implemented based on feature selection or feature extraction techniques. We propose here, a deep learning method based on Auto-Encoder (AE), allowing only required data to be distributed, and communicated. The results show that the proposed AE method surpasses other methods in terms of dimensionality reduction, performance and execution time.

In this paper, we propose a dynamic spectrum allocation (DSA) scheme DeepBlocks at the backdrop of sixth-generation (6G) communication networks that address the challenges of fixed spectrum allocations (FSA). The scheme exploits the advantages of deep-Q-network (DQN) and minimizes the search state explosion through a reward-penalty framework. A dynamic allocation of unallocated resource blocks (RBs) to mobile units (MUs) is carried out and once the allocation of RBs is complete, we integrate blockchain (BC) to record the transactional ledgers. The resource usage of MUs is recorded through smart contracts (SCs). We model the proposed scheme as a convex optimization problem, and subproblems are decomposed into a Pareto-optimal solution via Techebyecheff decomposition. In the simulation, we compare our scheme against FSA, and fifth-generation (5G) based DSA schemes like reinforcement learning (RL), deep neural networks (DNN)-based, and duelling DQN based schemes. The comparative analysis of 6G-DQN is modeled in terms of reward formulation, scalability of 6G-DQN-assisted DSA, and profit scenarios of BC-based allocation through intelligent channel control. The scheme proposes significant findings, with the best fit learning rate of 0.0001, and takes 500 episodes to converge to 60 total resource blocks. The servicing latency of the scheme is 272.4 ms, compared to 2010 ms in the duelling DQN approach. In spectrum allocation, an improvement of 26.32% is observed against non-DQN approaches, and 13.57% in the fairness parameter for spectrum allocation due to BC inclusion. The findings present the scheme efficacy for DSA over the aforementioned conventional approaches.

Over the last few years, there has been a great interest in the Internet of Things (IoT). This is mainly because the IoT interacts directly with people's everyday lives in critical applications, such as in smart homes and healthcare applications. IoT devices typically have a resource-constrained architecture, rendering them vulnerable to cyberattacks. Accordingly, smart devices and stored data need to be secured through lightweight and energy-efficient security solutions, which have been identified as the main challenge facing adoption of IoT systems. To address this problem, a disruptive technology (blockchain) has been foreseen by the industry and research community as being able to deliver secure, fast, reliable, and transparent solutions for IoT systems. Hence, this work investigates the adoption of lightweight blockchain technology, mainly as a method of securing IoT systems. Because hashing plays a major role in creating a robust blockchain structure, we select a number of different hash techniques to be executed on a Raspberry Pi device. In summation, this work provides a numerical study to evaluate the performance of well-known hash functions that can be used for lightweight blockchain-based IoT.

Cloud infrastructure capabilities, including massive, scalable and elastic computing resources, have led to the widespread adaption of Internet of Things (IoT) cloud-enabled services. This involves moving the storage and processing of sensitive IoT data to Cloud Service Providers (CSPs) that gain complete access to outsourced IoT data in the cloud. An efficient and lightweight Advanced Encryption Standard (AES) cryptosystem can play a major role in protecting IoT data from being exposed to CSPs by protecting the privacy of sensitive outsourced data. However, AES cryptosystems lack computation capabilities, which is a critical factor that prevents us taking full advantage of cloud computing services. When used with AES cryptosystems, Intel Software Guard Extensions (SGX) can provide a comprehensive solution to building secure data analytics framework for IoT-enabled application in various domains. In this paper, we develop a secure data analytics framework that relies on a hyper-integrated approach where both software- and hardware-based solutions are applied to protect and process sensitive outsourced data in the cloud.

Expectation maximization (EM) is a clustering-based machine learning algorithm that is widely used in many areas of science (e.g., bioinformatics and computer vision) to find maximum likelihood and maximum a posteriori estimates for models with latent variables. To deploy such an algorithm in cloud environments, security and privacy issues need be considered to avoid data breaches or abuses by external malicious parties or even by cloud service providers. However, the processing performance of the EM algorithm poses a challenge in terms of building a secure environment. This article describes an innovative and practical privacy-preserving EM algorithm for cloud systems that addresses this challenge, and estimates the EM parameters in an accurate and secure manner. Fully homomorphic encryption (FHE) is used to ensure the privacy of both the EM algorithm computations and the users' sensitive data in the cloud. A distributed-based approach is also proposed to overcome the overheads of FHE computations and ensure a fast convergence of the EM algorithm. The conducted experiments demonstrate a significant improvement in the convergence time of the distributed EM algorithm, while achieving a high level of accuracy and reducing the associated computational FHE overheads.