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See detailThe Impact Of Distributed Data Preprocessing On Automotive Data Streams
Tawakuli, Amal UL; Engel, Thomas UL

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 ▲]

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See detailPreventing Frame Fingerprinting in Controller Area Network Through Traffic Mutation
Buscemi, Alessio UL; Turcanu, Ion; Castignani, German UL et al

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 ▲]

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See detailOn Frame Fingerprinting and Controller Area Networks Security in Connected Vehicles
Buscemi, Alessio UL; Turcanu, Ion; Castignani, German et al

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 ▲]

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See detailTransforming IoT Data Preprocessing: A Holistic, Normalized and Distributed Approach
Tawakuli, Amal UL; Kaiser, Daniel UL; Engel, Thomas UL

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 ▲]

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See detailThe Effect of Noise Level on the Accuracy of Causal Discovery Methods with Additive Noise Models
Kap, Benjamin; Aleksandrova, Marharyta UL; Engel, Thomas UL

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 ▲]

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See detailCANMatch: A Fully Automated Tool for CAN Bus Reverse Engineering based on Frame Matching
Buscemi, Alessio UL; Turcanu, Ion; Castignani, German et al

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 ▲]

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See detailPoster: A Methodology for Semi-Automated CAN Bus Reverse Engineering
Buscemi, Alessio UL; Turcanu, Ion; German, Castignani et al

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 ▲]

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See detailA Near-Field-based TPMS Solution for Heavy Commercial Vehicle Environement
Rida, Ahmad UL; Soua, Ridha UL; Engel, Thomas UL

in 2021 IEEE 94th Vehicular Technology Conference (VTC2021-Fall) proceedings (2021, September 27)

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See detailEvaluation of TPMS Signal Propagation in a Heavy Commercial Vehicle Environement
Rida, Ahmad UL; Soua, Ridha UL; Engel, Thomas UL

in 2021 IEEE 94th Vehicular Technology Conference (VTC2021-Fall) proceedings (2021, September 27)

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See detailEvaluation of TPMS Signal Propagation in a Heavy Commercial Vehicle Environement
Rida, Ahmad UL; Ridha, Soua; Engel, Thomas UL

E-print/Working paper (2021)

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See detailA Near-Field-based TPMS Solution for Heavy Commercial Vehicle Environement
Rida, Ahmad UL; Soua, Ridha UL; Engel, Thomas UL

in 2021 IEEE 94th Vehicular Technology Conference - Final Program (2021, September)

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See detailFederated Learning-based Scheme for Detecting Passive Mobile Attackers in 5G Vehicular Edge Computing
Boualouache, Abdelwahab UL; Engel, Thomas UL

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 ▲]

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See detail𝑘-Pareto Optimality for Many-Objective Genetic Optimization
Ruppert, Jean; Aleksandrova, Marharyta UL; Engel, Thomas UL

Poster (2021, July)

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See detailIntelligent Misbehavior Detection System for Detecting False Position Attacks in Vehicular Networks
Hawlader, Faisal UL; Boualouache, Abdelwahab UL; Faye, Sébastien UL et al

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 ▲]

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See detailSynchronized Preprocessing of Sensor Data
Tawakuli, Amal UL; Kaiser, Daniel UL; Engel, Thomas UL

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 ▲]

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See detailAdaptive Content Seeding for Information-Centric Networking under High Topology Dynamics: Where You Seed Matters
Turcanu, Ion UL; Engel, Thomas UL; Sommer, Christoph

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 ▲]

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See detailConsortium Blockchain for Cooperative Location Privacy Preservation in 5G-enabled Vehicular Fog Computing
Boualouache, Abdelwahab UL; Sedjelmaci, Hichem; Engel, Thomas UL

in IEEE Transactions on Vehicular Technology (2021)

Privacy is a key requirement for connected vehicles. Cooperation between vehicles is mandatory for achieving location privacy preservation. However, non-cooperative vehicles can be a big issue to achieve ... [more ▼]

Privacy is a key requirement for connected vehicles. Cooperation between vehicles is mandatory for achieving location privacy preservation. However, non-cooperative vehicles can be a big issue to achieve this objective. To this end, we propose a novel monetary incentive scheme for cooperative location privacy preservation in 5G-enabled Vehicular Fog Computing. This scheme leverages a consortium blockchain-enabled fog layer and smart contracts to ensure a trusted and secure cooperative Pseudonym Changing Processes (PCPs). We also propose optimized smart contracts to reduce the monetary costs of vehicles while providing more location privacy preservation. Moreover, a resilient and lightweight Utility-based Delegated Byzantine Fault Tolerance (U-DBFT) consensus protocol is proposed to ensure fast and reliable block mining and validation. The performance analysis shows that our scheme has effective incentive techniques to stimulate non-cooperative vehicles and provides optimal monetary cost management and secure, private, fast validation of blocks. [less ▲]

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See detailSoftware-Defined Location Privacy Protection for Vehicular Networks
Boualouache, Abdelwahab UL; Soua, Ridha UL; Qiang, Tang et al

in Boualouache, Abdelwahab; Soua, Ridha; Qiang, Tang (Eds.) et al Machine Intelligence and Data Analytics for Sustainable Future Smart Cities (2021)

While the adoption of connected vehicles is growing, security and privacy concerns are still the key barriers raised by society. These concerns mandate automakers and standardization groups to propose ... [more ▼]

While the adoption of connected vehicles is growing, security and privacy concerns are still the key barriers raised by society. These concerns mandate automakers and standardization groups to propose convenient solutions for privacy preservation. One of the main proposed solutions is the use of Pseudonym-Changing Strategies (PCSs). However, ETSI has recently published a technical report which highlights the absence of standardized and efficient PCSs [1]. This alarming situation mandates an innovative shift in the way that the privacy of end-users is protected during their journey. Software Defined Networking (SDN) is emerging as a key 5G enabler to manage the network in a dynamic manner. SDN-enabled wireless networks are opening up new programmable and highly-flexible privacy-aware solutions. We exploit this paradigm to propose an innovative software-defined location privacy architecture for vehicular networks. The proposed architecture is context-aware, programmable, extensible, and able to encompass all existing and future pseudonym-changing strategies. To demonstrate the merit of our architecture, we consider a case study that involves four pseudonym-changing strategies, which we deploy over our architecture and compare with their static implementations. We also detail how the SDN controller dynamically switches between the strategies according to the context. [less ▲]

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See detailOut-of-the-box Multipath TCP as a Tor Transport Protocol: Performance and Privacy Implications
de La Cadena Ramos, Augusto Wladimir UL; Kaiser, Daniel UL; Panchenko, Andriy UL et al

in 19th IEEE International Symposium on Network Computing and Applications (IEEE NCA 2020) (2020, November 25)

Detailed reference viewed: 121 (4 UL)