213 research outputs found

    A survey on pseudonym changing strategies for Vehicular Ad-Hoc Networks

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    The initial phase of the deployment of Vehicular Ad-Hoc Networks (VANETs) has begun and many research challenges still need to be addressed. Location privacy continues to be in the top of these challenges. Indeed, both of academia and industry agreed to apply the pseudonym changing approach as a solution to protect the location privacy of VANETs'users. However, due to the pseudonyms linking attack, a simple changing of pseudonym shown to be inefficient to provide the required protection. For this reason, many pseudonym changing strategies have been suggested to provide an effective pseudonym changing. Unfortunately, the development of an effective pseudonym changing strategy for VANETs is still an open issue. In this paper, we present a comprehensive survey and classification of pseudonym changing strategies. We then discuss and compare them with respect to some relevant criteria. Finally, we highlight some current researches, and open issues and give some future directions

    dSDiVN: a distributed Software-Defined Networking architecture for Infrastructure-less Vehicular Networks

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    In the last few years, the emerging network architecture paradigm of Software-Defined Networking (SDN), has become one of the most important technology to manage large scale networks such as Vehicular Ad-hoc Networks (VANETs). Recently, several works have shown interest in the use of SDN paradigm in VANETs. SDN brings flexibility, scalability and management facility to current VANETs. However, almost all of proposed Software-Defined VANET (SDVN) architectures are infrastructure-based. This paper will focus on how to enable SDN in infrastructure-less vehicular environments. For this aim, we propose a novel distributed SDN-based architecture for uncovered infrastructure-less vehicular scenarios. It is a scalable cluster-based architecture with distributed mobile controllers and a reliable fall back recovery mechanism based on self-organized clustering and failure anticipation.Comment: 12 pages, 5 figures, accepted in I4CS201

    HPDM: A Hybrid Pseudonym Distribution Method for Vehicular Ad-hoc Networks

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    AbstractProtecting the location privacy of drivers is still one of the main challenges in Vehicular Ad-hoc Networks (VANETs). The changing of pseudonym is commonly accepted as a solution to this problem. The pseudonyms represent fake vehicle identifiers. Roadside Units (RSUs) play a central role in the existing pseudonyms distribution solutions. Indeed, the VANET area should totally be covered by RSUs in order to satisfy the demand of vehicles in terms of pseudonyms. However, the total coverage is costly and hard to be achieved, especially in the first phase of VANETs deployment. In addition, RSUs could be overloaded due to the large number of pseudonyms requests that could be received from vehicles. In this paper, we propose a new hybrid pseudonyms distribution method, called HPDM that relies not only on RSUs but also on vehicles to perform the pseudonyms distribution. The analysis demonstrate that HPDM is privacy and accountability preserving. The performance evaluation of the proposed method is carried out using veins framework based on OMNet++ network simulator and SUMO mobility engine and shows its feasibility

    Enhanced relay selection decision for cooperative communication in energy constrained networks

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    International audienceMost of current works related to relay selection algorithms in cooperative communications use the Channel State Information (CSI) to decide whether to use one or another neighbor as a relay. Therefore in wireless sensor networks where the energy is the major constraint such algorithms may lead to quick battery drain of the nodes having the best links. In this paper we propose to enhance the relay selection decision process by taking into account the energy metric in addition to CSI. The results show that we can redistribute the consumed energy when we use the energy as a relay selection metric

    COSMIC: A Cooperative MAC Protocol for WSN with Minimal Control Messages

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    International audienceOver the last decade cooperative communication in wireless sensor networks (WSN) received much attention. A lot of works have been done to propose a MAC layer that supports cooperative relaying. The majority of these works tried to adapt the IEEE 802.11 MAC protocol to sensor networks. The adapted protocols use a lot of overhead (such as the use of RTS/CTS as well as other messages used to allow cooperation) that consumes energy. In this paper we propose a CSMA/CA based MAC protocol that supports cooperative communication with a minimum overhead: COSMIC (A Cooperative MAC Protocol for WSN with Minimal Control Messages). Relay selection in this new protocol is performed using both the channel state information (CSI) and the remaining energy. Simulation results show that COSMIC is able to increase the network lifetime by 25%

    A Combined Relay-Selection and Routing Protocol for Cooperative Wireless Sensor Networks

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    International audienceIn wireless sensor networks several constraints decrease communications performances. In fact, channel randomness and energy restrictions make classical routing protocols inefficient. Therefore, the design of new routing protocols that cope with these constraints become mandatory. The main objective of this paper is to present a multi-objective routing algorithm RBCR that computes routing path based on the energy consumption and channel qualities. Additionally, the channel qualities are evaluated based on the presence of relay nodes. Compared to AODV and AODV associated to a cooperative MAC protocol, RBCR provides better performances in term of delivery ratio, power consumption and traffic load

    A Lightweight 5G-V2X Intra-slice Intrusion Detection System Using Knowledge Distillation

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    As the automotive industry grows, modern vehicles will be connected to 5G networks, creating a new Vehicular-to-Everything (V2X) ecosystem. Network Slicing (NS) supports this 5G-V2X ecosystem by enabling network operators to flexibly provide dedicated logical networks addressing use case specific-requirements on top of a shared physical infrastructure. Despite its benefits, NS is highly vulnerable to privacy and security threats, which can put Connected and Automated Vehicles (CAVs) in dangerous situations. Deep Learning-based Intrusion Detection Systems (DL-based IDSs) have been proposed as the first defense line to detect and report these attacks. However, current DL-based IDSs are processing and memory-consuming, increasing security costs and jeopardizing 5G-V2X acceptance. To this end, this paper proposes a lightweight intrusion detection scheme for 5G-V2X sliced networks. Our scheme leverages DL and Knowledge Distillation (KD) for training in the cloud and offloading knowledge to slice-tailored lightweight DL models running on CAVs. Our results show that our scheme provides an optimal trade-off between detection accuracy and security overhead. Specifically, it can reduce security overhead in computation and memory complexity to more than 50% while keeping almost the same performance as heavy DL-based IDSs

    On-Demand Security Framework for 5GB Vehicular Networks

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    Building accurate Machine Learning (ML) attack detection models for 5G and Beyond (5GB) vehicular networks requires collaboration between Vehicle-to-Everything (V2X) nodes. However, while operating collaboratively, ensuring the ML model's security and data privacy is challenging. To this end, this article proposes a secure and privacy-preservation on-demand framework for building attack-detection ML models for 5GB vehicular networks. The proposed framework emerged from combining 5GB technologies, namely, Federated Learning (FL), blockchain, and smart contracts to ensure fair and trusted interactions between FL servers (edge nodes) with FL workers (vehicles). Moreover, it also provides an efficient consensus algorithm with an intelligent incentive mechanism to select the best FL workers that deliver highly accurate local ML models. Our experiments demonstrate that the framework achieves higher accuracy on a well-known vehicular dataset with a lower blockchain consensus time than related solutions. Specifically, our framework enhances the accuracy by 14% and decreases the consensus time, at least by 50%, compared to related works. Finally, this article discusses the framework's key challenges and potential solutions

    Edge Computing enabled Intrusion Detection for C-V2X Networks using Federated Learning

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    Intrusion detection systems (IDS) have already demonstrated their effectiveness in detecting various attacks in cellular vehicle-to-everything (C-V2X) networks, especially when using machine learning (ML) techniques. However, it has been shown that generating ML-based models in a centralized way consumes a massive quantity of network resources, such as CPU/memory and bandwidth, which may represent a critical issue in such networks. To avoid this problem, the new concept of Federated Learning (FL) emerged to build ML-based models in a distributed and collaborative way. In such an approach, the set of nodes, e.g., vehicles or gNodeB, collaborate to create a global ML model trained across these multiple decentralized nodes, each one with its respective data samples that are not shared with any other nodes. In this way, FL enables, on the one hand, data privacy since sharing data with a central location is not always feasible and, on the other hand, network overhead reduction. This paper designs a new IDS for C-V2X networks based on FL. It leverages edge computing to not only build a prediction model in a distributed way but also to enable low-latency intrusion detection. Moreover, we build our FL-based IDS on top of the well-known CIC-IDS2018 dataset, which includes the main network attacks. Noting that, we first perform feature engineering on the dataset using the ANOVA method to consider only the most informative features. Simulation results show the efficiency of our system compared to the existing solutions in terms of attack detection accuracy while reducing network resource consumption

    Edge Computing-enabled Intrusion Detection for C-V2X Networks using Federated Learning

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    Intrusion detection systems (IDS) have already demonstrated their effectiveness in detecting various attacks in cellular vehicle-to-everything (C-V2X) networks, especially when using machine learning (ML) techniques. However, it has been shown that generating ML-based models in a centralized way consumes a massive quantity of network resources, such as CPU/memory and bandwidth, which may represent a critical issue in such networks. To avoid this problem, the new concept of Federated Learning (FL) emerged to build ML-based models in a distributed and collaborative way. In such an approach, the set of nodes, e.g., vehicles or gNodeB, collaborate to create a global ML model trained across these multiple decentralized nodes, each one with its respective data samples that are not shared with any other nodes. In this way, FL enables, on the one hand, data privacy since sharing data with a central location is not always feasible and, on the other hand, network overhead reduction. This paper designs a new IDS for C-V2X networks based on FL. It leverages edge computing to not only build a prediction model in a distributed way but also to enable low-latency intrusion detection. Moreover, we build our FL-based IDS on top of the well-known CIC-IDS2018 dataset, which includes the main network attacks. Noting that, we first perform feature engineering on the dataset using the ANOVA method to consider only the most informative features. Simulation results show the efficiency of our system compared to the existing solutions in terms of attack detection accuracy while reducing network resource consumption
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