Results 1-20 of 203.
![]() Boualouache, Abdelwahab ![]() ![]() in IEEE Communications Surveys and Tutorials (2022) Advances in Vehicle-to-Everything (V2X) technology and onboard sensors have significantly accelerated deploying Connected and Automated Vehicles (CAVs). Integrating V2X with 5G has enabled Ultra-Reliable ... [more ▼] Advances in Vehicle-to-Everything (V2X) technology and onboard sensors have significantly accelerated deploying Connected and Automated Vehicles (CAVs). Integrating V2X with 5G has enabled Ultra-Reliable Low Latency Communications (URLLC) to CAVs. However, while communication performance has been enhanced, security and privacy issues have increased. Attacks have become more aggressive, and attackers have become more strategic. Public Key Infrastructure (PKI) proposed by standardization bodies cannot solely defend against these attacks. Thus, in complementary of that, sophisticated systems should be designed to detect such attacks and attackers. Machine Learning (ML) has recently emerged as a key enabler to secure future roads. Various V2X Misbehavior Detection Systems (MDSs) have adopted this paradigm. However, analyzing these systems is a research gap, and developing effective ML-based MDSs is still an open issue. To this end, this paper comprehensively surveys and classifies ML-based MDSs as well as discusses and analyses them from security and ML perspectives. It also provides some learned lessons and recommendations for guiding the development, validation, and deployment of ML-based MDSs. Finally, this paper highlighted open research and standardization issues with some future directions. [less ▲] Detailed reference viewed: 17 (13 UL)![]() Boualouache, Abdelwahab ![]() in IEEE Internet of Things Journal (2022) 5G Vehicle-to-Everything (5G-V2X) communications will play a vital role in the development of the automotive industry. Indeed and thanks to the Network Slicing (NS) concept of 5G and beyond networks (B5G ... [more ▼] 5G Vehicle-to-Everything (5G-V2X) communications will play a vital role in the development of the automotive industry. Indeed and thanks to the Network Slicing (NS) concept of 5G and beyond networks (B5G), unprecedented new vehicular use–cases can be supported on top of the same physical network. NS promises to enable the sharing of common network infrastructure and resources while ensuring strict traffic isolation and providing necessary network resources to each NS. However, enabling NS in vehicular networks brings new security challenges and requirements that automotive or 5G standards have not yet addressed. Attackers can exploit the weakest link in the slicing chain, connected and automated vehicles, to violate the slice isolation and degrade its performance. Furthermore, these attacks can be more powerful, especially if they are produced in cross-border areas of two countries, which require an optimal network transition from one operator to another. Therefore, this article aims to provide an overview of newly enabled 5G-V2X slicing use cases and their security issues while focusing on cross-border slicing attacks. It also presents the open security issues of 5G-V2X slicing and identifies some opportunities. [less ▲] Detailed reference viewed: 70 (14 UL)![]() Boualouache, Abdelwahab ![]() ![]() in Boualouache, Abdelwahab; Engel, Thomas (Eds.) 2022 IEEE 96th Vehicular Technology Conference: (VTC2022-Fall) (2022, September) As a leading enabler of 5G, Network Slicing (NS) aims at creating multiple virtual networks on the same shared and programmable physical infrastructure. Integrated with 5G-Vehicle-to-Everything (V2X ... [more ▼] As a leading enabler of 5G, Network Slicing (NS) aims at creating multiple virtual networks on the same shared and programmable physical infrastructure. Integrated with 5G-Vehicle-to-Everything (V2X) technology, NS enables various isolated 5G-V2X networks with different requirements such as autonomous driving and platooning. This combination has generated new attack surfaces against Connected and Automated Vehicles (CAVs), leading them to road hazards and putting users' lives in danger. More specifically, such attacks can either intra-slice targeting the internal service within each V2X Network Slice (V2X-NS) or inter-slice targeting the cross V2X-NSs and breaking the isolation between them. However, detecting such attacks is challenging, especially inter-slice V2X attacks where security mechanisms should maintain privacy preservation and NS isolation. To this end, this paper addresses detecting inter-slice V2X attacks. To do so, we leverage both Virtual Security as a Service (VSaS) concept and Deep learning (DL) together with Federated learning (FL) to deploy a set of DL-empowered security Virtual Network Functions (sVNFs) over V2X-NSs. Our privacy preservation scheme is hierarchical and supports FL-based collaborative learning. It also integrates a game-theory-based mechanism to motivate FL clients (CAVs) to provide high-quality DL local models. We train, validate, and test our scheme using a publicly available dataset. The results show our scheme's accuracy and efficiency in detecting inter-slice V2X attacks. [less ▲] Detailed reference viewed: 35 (2 UL)![]() Tawakuli, Amal ![]() ![]() in 2022 IEEE 96th Vehicular Technology Conference: (VTC2022-Fall) (2022, September) Vehicles have transformed into sophisticated com- puting machines that not only serve the objective of transporta- tion from point A to point B but serve other objectives including improved experience ... [more ▼] Vehicles have transformed into sophisticated com- puting machines that not only serve the objective of transporta- tion from point A to point B but serve other objectives including improved experience, safer journey, automated and more efficient and sustainable transportation. With such sophistication comes complex applications and enormous volumes of data generated from diverse types of vehicle sensors and components. Automotive data is not sedentary but moves from the edge (the vehicle) to the cloud (e.g., infrastructure of the vehicle manufacturers, national highway agencies, insurance companies, etc.). The exponential increase in data volume and variety generated in modern vehicles far exceeds the rate of infrastructure scaling and expansion. To mitigate this challenge, the computational and storage capacities of vehicle components can be leveraged to perform in-vehicle operations on the data to either prepare and transform (prepro- cess) the data or extract information from (process) the data. This paper focuses on distributing data preprocessing to the vehicle and highlights the benefits and impact of the distribution including on the consumption of resources (e.g., energy). [less ▲] Detailed reference viewed: 52 (3 UL)![]() Boualouache, Abdelwahab ![]() in 2022 IEEE 95th Vehicular Technology Conference: (VTC2022-Spring) (2022, August 25) Connected and Automated Vehicles (CAVs) represent one of the main verticals of 5G to provide road safety, road traffic efficiency, and user convenience. As a key enabler of 5G, Network Slicing (NS) aims ... [more ▼] Connected and Automated Vehicles (CAVs) represent one of the main verticals of 5G to provide road safety, road traffic efficiency, and user convenience. As a key enabler of 5G, Network Slicing (NS) aims to create Vehicle-to-Everything (V2X) network slices with different network requirements on a shared and programmable physical infrastructure. However, NS has generated new network threats that might target CAVs leading to road hazards. More specifically, such attacks may target either the inner functioning of each V2X-NS (intra-slice) or break the NS isolation. In this paper, we aim to deal with the raised question of how to detect intra-slice V2X attacks. To do so, we leverage both Virtual Security as a Service (VSaS) concept and deep learning (DL) to deploy a set of DL-empowered security Virtual Network Functions (sVNFs) within V2X-NSs. These sVNFs are in charge of detecting such attacks, thanks to a DL model that we also build in this work. The proposed DL model is trained, validated, and tested using a publicly available dataset. The results show the efficiency and accuracy of our scheme to detect intra-slice V2X attacks. [less ▲] Detailed reference viewed: 36 (3 UL)![]() Buscemi, Alessio ![]() ![]() in IEEE ICC 2022 Workshop - DDINS, Seoul 16-20 May 2022 (2022, May) The continuous increase of connectivity in commercial vehicles is leading to a higher number of remote access points to the Controller Area Network (CAN) – the most popular in-vehicle network system. This ... [more ▼] The continuous increase of connectivity in commercial vehicles is leading to a higher number of remote access points to the Controller Area Network (CAN) – the most popular in-vehicle network system. This factor, coupled with the absence of encryption in the communication protocol, poses serious threats to the security of the CAN bus. Recently, it has been demonstrated that CAN data can be reverse engineered via frame fingerprinting, i.e., identification of frames based on statistical traffic analysis. Such a methodology allows fully remote decoding of in-vehicle data and paves the way for remote pre-compiled vehicle-agnostic attacks. In this work, we propose a first solution against CAN frame fingerprinting based on mutating the traffic without applying modifications to the CAN protocol. The results show that the proposed methodology halves the accuracy of CAN frame fingerprinting. [less ▲] Detailed reference viewed: 149 (20 UL)![]() Buscemi, Alessio ![]() in IEEE Consumer Communications & Networking Conference, Virtual Conference 8-11 January 2022 (2022, January) Modern connected vehicles are equipped with a large number of sensors, which enable a wide range of services that can improve overall traffic safety and efficiency. However, remote access to connected ... [more ▼] Modern connected vehicles are equipped with a large number of sensors, which enable a wide range of services that can improve overall traffic safety and efficiency. However, remote access to connected vehicles also introduces new security issues affecting both inter and intra-vehicle communications. In fact, existing intra-vehicle communication systems, such as Controller Area Network (CAN), lack security features, such as encryption and secure authentication for Electronic Control Units (ECUs). Instead, Original Equipment Manufacturers (OEMs) seek security through obscurity by keeping secret the proprietary format with which they encode the information. Recently, it has been shown that the reuse of CAN frame IDs can be exploited to perform CAN bus reverse engineering without physical access to the vehicle, thus raising further security concerns in a connected environment. This work investigates whether anonymizing the frames of each newly released vehicle is sufficient to prevent CAN bus reverse engineering based on frame ID matching. The results show that, by adopting Machine Learning techniques, anonymized CAN frames can still be fingerprinted and identified in an unknown vehicle with an accuracy of up to 80 %. [less ▲] Detailed reference viewed: 138 (19 UL)![]() Tawakuli, Amal ![]() ![]() ![]() in The Fifth International Workshop on Data: Acquisition To Analysis (2022) Data preprocessing is an integral part of Artificial Intelligence (AI) pipelines. It transforms raw data into input data that fulfill algorithmic criteria and improve prediction accuracy. As the adoption ... [more ▼] Data preprocessing is an integral part of Artificial Intelligence (AI) pipelines. It transforms raw data into input data that fulfill algorithmic criteria and improve prediction accuracy. As the adoption of Internet of Things (IoT) gains more momentum, the data volume generated from the edge is exponentially increasing that far exceeds any expansion of infrastructure. Social responsibilities and regulations (e.g., GDPR) must also be adhered when handling IoT data. In addition, we are currently witnessing a shift towards distributing AI to the edge. The aforementioned reasons render the distribution of data preprocessing to the edge an urgent requirement. In this paper, we introduce a modern data preprocessing framework that consists of two main parts. Part1 is a design tool that reduces the complexity and costs of the data preprocessing phase for AI via generalization and normalization. The design tool is a standard template that maps specific techniques into abstract categories and highlights dependencies between them. In addition, it presents a holistic notion of data preprocessing that is not limited to data cleaning. The second part is an IoT tool that adopts the edge-cloud collaboration model to progressively improve the quality of the data. It includes a synchronization mechanism that ensures adaptation to changes in data characteristics and a coordination mechanism that ensures correct and complete execution of preprocessing plans between the cloud and the edge. The paper includes an empirical analysis of the framework using a developed prototype and an automotive use-case. Our results demonstrate reductions in resource consumption (e.g., energy, bandwidth) while maintaining the value and integrity of the data. [less ▲] Detailed reference viewed: 48 (1 UL)![]() ; Aleksandrova, Marharyta ![]() ![]() in Communications in Computer and Information Science (2022), 1530 In recent years a lot of research was conducted within the area of causal inference and causal learning. Many methods were developed to identify the cause-effect pairs. These methods also proved their ... [more ▼] In recent years a lot of research was conducted within the area of causal inference and causal learning. Many methods were developed to identify the cause-effect pairs. These methods also proved their ability to successfully determine the direction of causal relationships from observational real-world data. Yet in bivariate situations, causal discovery problems remain challenging. A class of methods, that also allows tackling the bivariate case, is based on Additive Noise Models (ANMs). Unfortunately, one aspect of these methods has not received much attention until now: what is the impact of different noise levels on the ability of these methods to identify the direction of the causal relationship? This work aims to bridge this gap with the help of an empirical study. We consider a bivariate case and two specific methods Regression with Subsequent Independence Test and Identification using Conditional Variances. We perform a set of experiments with an exhaustive range of ANMs where the additive noises’ levels gradually change from 1% to 10000% of the causes’ noise level (the latter remains fixed). Additionally, we consider several different types of distributions as well as linear and non-linear ANMs. The results of the experiments show that these causal discovery methods can fail to capture the true causal direction for some levels of noise. [less ▲] Detailed reference viewed: 81 (2 UL)![]() Buscemi, Alessio ![]() in IEEE Transactions on Vehicular Technology (2021) Controller Area Network (CAN) is the most frequently used in-vehicle communication system in the automotive industry today. The communication inside the CAN bus is typically encoded using proprietary ... [more ▼] Controller Area Network (CAN) is the most frequently used in-vehicle communication system in the automotive industry today. The communication inside the CAN bus is typically encoded using proprietary formats in order to prevent easy access to the information exchanged on the bus. However, it is still possible to decode this information through reverse engineering, performed either manually or via automated tools. Existing automated CAN bus reverse engineering methods are still time-consuming and require some manual effort, i.e., to inject diagnostic messages in order to trigger specific responses. In this paper, we propose CANMatch a fully automated CAN bus reverse engineering framework that does not require any manual effort and significantly decreases the execution time by exploiting the reuse of CAN frames across different vehicle models. We evaluate the proposed solution on a dataset of CAN logs, or traces, related to 479 vehicles from 29 different automotive manufacturers, demonstrating its improved performance with respect to the state of the art. [less ▲] Detailed reference viewed: 130 (22 UL)![]() Buscemi, Alessio ![]() Poster (2021, November) Semi-automated Controller Area Network (CAN) reverse engineering has been shown to provide decoding accuracy comparable to the manual approach, while reducing the time required to decode signals. However ... [more ▼] Semi-automated Controller Area Network (CAN) reverse engineering has been shown to provide decoding accuracy comparable to the manual approach, while reducing the time required to decode signals. However, current approaches are invasive, as they make use of diagnostic messages injected through the On-Board Diagnostics (OBD-II) port and often require a high amount of non-CAN external data. In this work, we present a non-invasive universal methodology for semi-automated CAN bus reverse engineering, which is based on the taxonomy of CAN signals. The data collection is simplified and its time reduced from the current standard of up to an hour to few minutes. A mean recall of around 80 % is obtained. [less ▲] Detailed reference viewed: 110 (28 UL)![]() Rida, Ahmad ![]() ![]() ![]() in 2021 IEEE 94th Vehicular Technology Conference (VTC2021-Fall) proceedings (2021, September 27) Detailed reference viewed: 33 (0 UL)![]() Rida, Ahmad ![]() ![]() ![]() in 2021 IEEE 94th Vehicular Technology Conference (VTC2021-Fall) proceedings (2021, September 27) Detailed reference viewed: 46 (1 UL)![]() Rida, Ahmad ![]() ![]() E-print/Working paper (2021) Detailed reference viewed: 84 (7 UL)![]() Rida, Ahmad ![]() ![]() ![]() in 2021 IEEE 94th Vehicular Technology Conference - Final Program (2021, September) Detailed reference viewed: 46 (2 UL)![]() Boualouache, Abdelwahab ![]() ![]() in Annals of Telecommunications (2021) Detecting passive attacks is always considered difficult in vehicular networks. Passive attackers can eavesdrop on the wireless medium to collect beacons. These beacons can be exploited to track the ... [more ▼] Detecting passive attacks is always considered difficult in vehicular networks. Passive attackers can eavesdrop on the wireless medium to collect beacons. These beacons can be exploited to track the positions of vehicles not only to violate their location privacy but also for criminal purposes. In this paper, we propose a novel federated learning-based scheme for detecting passive mobile attackers in 5G Vehicular Edge Computing. We first identify a set of strategies that can be used by attackers to efficiently track vehicles without being visually detected. We then build an efficient Machine Learning (ML) model to detect tracking attacks based only on the receiving beacons. Our scheme enables Federated Learning (FL) at the edge to ensure collaborative learning while preserving the privacy of vehicles. Moreover, FL clients use a semi-supervised learning approach to ensure accurate self-labeling. Our experiments demonstrate the effectiveness of our proposed scheme to detect passive mobile attackers quickly and with high accuracy. Indeed, only 20 received beacons are required to achieve 95\% accuracy. This accuracy can be achieved within 60 FL rounds using 5 FL clients in each FL round. The obtained results are also validated through simulations. [less ▲] Detailed reference viewed: 78 (14 UL)![]() ; Aleksandrova, Marharyta ![]() ![]() Poster (2021, July) Detailed reference viewed: 58 (2 UL)![]() Hawlader, Faisal ![]() ![]() ![]() in Hawlader, Faisal; Boualouache, Abdelwahab; Faye, Sébastien (Eds.) et al The 2021 IEEE International Conference on Communications (the 4th Workshop on 5G and Beyond Wireless Security) (2021, June) Position falsification attacks are one of the most dangerous internal attacks in vehicular networks. Several Machine Learning-based Misbehavior Detection Systems (ML-based MDSs) have recently been proposed ... [more ▼] Position falsification attacks are one of the most dangerous internal attacks in vehicular networks. Several Machine Learning-based Misbehavior Detection Systems (ML-based MDSs) have recently been proposed to detect these attacks and mitigate their impact. However, existing ML-based MDSs require numerous features, which increases the computational time needed to detect attacks. In this context, this paper introduces a novel ML-based MDS for the early detection of position falsification attacks. Based only on received positions, our system provides real-time and accurate predictions. Our system is intensively trained and tested using a publicly available data set, while its validation is done by simulation. Six conventional classification algorithms are applied to estimate and construct the best model based on supervised learning. The results show that the proposed system can detect position falsification attacks with almost 100% accuracy. [less ▲] Detailed reference viewed: 258 (91 UL)![]() Tawakuli, Amal ![]() ![]() ![]() in 2020 IEEE International Conference on Big Data (2021, March 19) Sensor data whether collected for machine learning, deep learning or other applications must be preprocessed to fit input requirements or improve performance and accuracy. Data preparation is an expensive ... [more ▼] Sensor data whether collected for machine learning, deep learning or other applications must be preprocessed to fit input requirements or improve performance and accuracy. Data preparation is an expensive, resource consuming and complex phase often performed centrally on raw data for a specific application. The dataflow between the edge and the cloud can be enhanced in terms of efficiency, reliability and lineage by preprocessing the datasets closer to their data sources. We propose a dedicated data preprocessing framework that distributes preprocessing tasks between a cloud stage and two edge stages to create a dataflow with progressively improving quality. The framework handles heterogenous data and dynamic preprocessing plans simultaneously targeting diverse applications and use cases from different domains. Each stage autonomously executes sensor specific preprocessing plans in parallel while synchronizing the progressive execution and dynamic updates of the preprocessing plans with the other stages. Our approach minimizes the workload on central infrastructures and reduces the resources used for transferring raw data from the edge. We also demonstrate that preprocessing data can be sensor specific rather than application specific and thus can be performed prior to knowing a specific application. [less ▲] Detailed reference viewed: 103 (11 UL)![]() Turcanu, Ion ![]() ![]() in IEEE Vehicular Technology Magazine (2021), 16(2), High-fidelity content distribution and other emerging applications of 5G and beyond-5G mobile broadband networking can put massive load on the core and Radio Access Network (RAN). To address this, direct ... [more ▼] High-fidelity content distribution and other emerging applications of 5G and beyond-5G mobile broadband networking can put massive load on the core and Radio Access Network (RAN). To address this, direct Device to Device (D2D) communication has recently become a first-class citizen of these networks. While Information-Centric Vehicular Networking (ICVN) based on fog computing can indeed exploit such D2D links to alleviate the load on the RAN by proactively seeding content in the network, it has been shown that such seeding can cause even more load if performed where not needed. In addition, trying to determine where to seed content often causes additional load, negating the benefit of seeding. In this work, we therefore propose to adaptively seed fog nodes based on a purely virtual clustering approach. Here, vehicles are unaware of clustering decisions, thus no longer requiring an explicit exchange of control messages. We show that the benefit of such an adaptive approach goes beyond simply being able to flexibly trade off performance metrics versus each other: instead, it can consistently lower the load on the RAN link. We also show that this property even holds if node location information is only available as coarsely-grained as macro-scale grid cells. [less ▲] Detailed reference viewed: 143 (20 UL) |
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