Biblio

Vehicular Ad Hoc Networks (VANETs) have a strategic goal to achieve service delivery in roads and smart cities, considering the integration and communication between vehicles, sensors and fixed road-side components (routers, gateways and services). VANETs have singular characteristics such as fast mobile nodes, self-organization, distributed network and frequently changing topology. Despite the recent evolution of VANETs, security, data integrity and users privacy information are major concerns, since attacks prevention is still open issue. One of the most dangerous attacks in VANETs is the Sybil, which forges false identities in the network to disrupt compromise the communication between the network nodes. Sybil attacks affect the service delivery related to road safety, traffic congestion, multimedia entertainment and others. Thus, VANETs claim for security mechanism to prevent Sybil attacks. Within this context, this paper proposes a mechanism, called SyDVELM, to detect Sybil attacks in VANETs based on artificial intelligence techniques. The SyDVELM mechanism uses Extreme Learning Machine (ELM) with occasional features of vehicular nodes, minimizing the identification time, maximizing the detection accuracy and improving the scalability. The results suggest that the suitability of SyDVELM mechanism to mitigate Sybil attacks and to maintain the service delivery in VANETs.

One of the main concerns of Cloud storage solutions is to offer the availability to the end user. Thus, addressing the mobility needs and device's variety has emerged as a major challenge. At first, data should be synchronized automatically and continuously when the user moves from one equipment to another. Secondly, the Cloud service should offer to the owner the possibility to share data with specific users. The paper's goal is to develop a secure framework that ensures file synchronization with high quality and minimal resource consumption. As a first step towards this goal, we propose the SyncDS protocol with its associated architecture. The synchronization protocol efficiency raises through the choice of the used networking protocol as well as the strategy of changes detection between two versions of file systems located in different devices. Our experiment results show that adopting the Hierarchical Hash Tree to detect the changes between two file systems and adopting the WebSocket protocol for the data exchanges improve the efficiency of the synchronization protocol.

Often considered as the brain of an industrial process, Industrial control systems are presented as the vital part of today's critical infrastructure due to their crucial role in process control and monitoring. Any failure or error in the system will have a considerable damage. Their openness to the internet world raises the risk related to cyber-attacks. Therefore, it's necessary to consider cyber security challenges while designing an ICS in order to provide security services such as authentication, integrity, access control and secure communication channels. To implement such services, it's necessary to provide an efficient key management system (KMS) as an infrastructure for all cryptographic operations, while preserving the functional characteristics of ICS. In this paper we will analyze existing KMS and their suitability for ICS, then we propose a new KMS based on Identity Based Cryptography (IBC) as a better alternative to traditional KMS. In our proposal, we consider solving two security problems in IBC which brings it up to be more suitable for ICS.

In Vehicular networks, privacy, especially the vehicles' location privacy is highly concerned. Several pseudonymous based privacy protection mechanisms have been established and standardized in the past few years by IEEE and ETSI. However, vehicular networks are still vulnerable to Sybil attack. In this paper, a Sybil attack detection method based on k-Nearest Neighbours (kNN) classification algorithm is proposed. In this method, vehicles are classified based on the similarity in their driving patterns. Furthermore, the kNN methods' high runtime complexity issue is also optimized. The simulation results show that our detection method can reach a high detection rate while keeping error rate low.

Vehicular networks have been drawing special atten- tion in recent years, due to its importance in enhancing driving experience and improving road safety in future smart city. In past few years, several security services, based on cryptography, PKI and pseudonymous, have been standardized by IEEE and ETSI. However, vehicular networks are still vulnerable to various attacks, especially Sybil attack. In this paper, a Support Vector Machine (SVM) based Sybil attack detection method is proposed. We present three SVM kernel functions based classifiers to distinguish the malicious nodes from benign ones via evaluating the variance in their Driving Pattern Matrices (DPMs). The effectiveness of our proposed solution is evaluated through extensive simulations based on SUMO simulator and MATLAB. The results show that the proposed detection method can achieve a high detection rate with low error rate even under a dynamic traffic environment.