Evaluation of PTP Security Controls on gPTP

in 28th IEEE International Symposium on Computers and Communications (ISCC 2023), Tunis, July 2023 (2023, July 09)

In recent years, the scientific community has been focusing on deterministic Ethernet, which has helped drive the adoption of Time-Sensitive Networking (TSN) standards. Precision Time Protocol (PTP ... [more ▼]

In recent years, the scientific community has been focusing on deterministic Ethernet, which has helped drive the adoption of Time-Sensitive Networking (TSN) standards. Precision Time Protocol (PTP), specified in IEEE1588, is a TSN standard that enables network devices to be synchronized with a degree of precision that is noticeably higher than other Ethernet synchronization protocols. Generic Precision Time Protocol (gPTP), a profile of PTP, is designed to have low latency and jitter, which makes it suitable for industrial applications. However, like PTP, gPTP does not have any built-in security measures. In this work, we assess the efficacy of additional security mechanisms that were suggested for inclusion in IEEE 1588 (PTP) 2019. The analysis consists of implementing these security mechanisms on a physical gPTP-capable testbed and evaluating them on several high-risk attacks against gPTP. [less ▲]

Assessing the Impact of Attacks on an Automotive Ethernet Time Synchronization Testbed

in Fotouhi, Mahdi; Buscemi, Alessio; Boualouache, Abdelwahab (Eds.) et al 2023 IEEE Vehicular Networking Conference (VNC), Istanbul 26-28 April 2023 (2023, April)

Time Sensitive Network (TSN) standards are gaining traction in the scientific community and automotive Original Equipment Manufacturers (OEMs) due their promise of deterministic Ethernet networking. Among ... [more ▼]

Time Sensitive Network (TSN) standards are gaining traction in the scientific community and automotive Original Equipment Manufacturers (OEMs) due their promise of deterministic Ethernet networking. Among these standards, Generalized Precision Time Protocol (gPTP) - IEEE 802.1AS - allows network devices to be synchronized with a precision far higher than other synchronization standards, such as Network Time Protocol (NTP). gPTP is a profile of Precision Time Protocol (PTP) which, due to its robustness to delay variations, has been designated for automotive applications. Nonetheless, gPTP was designed without security controls, which makes it vulnerable to a number of attacks. This work reveals a critical vulnerability caused by a common implementation practice that opens the door to spoofing attacks on gPTP. To assess the impact of this vulnerability, we built two real gPTP-capable testbeds. Our results show high risks of this vulnerability destabilizing the system functionality. [less ▲]

A Survey on Privacy-preserving Electronic Toll Collection Schemes for Intelligent Transportation Systems

in IEEE Transactions on Intelligent Transportation Systems (2023)

As part of Intelligent Transportation Systems (ITS), Electronic toll collection (ETC) is a type of toll collection system (TCS) which is getting more and more popular as it can not only help to finance ... [more ▼]

As part of Intelligent Transportation Systems (ITS), Electronic toll collection (ETC) is a type of toll collection system (TCS) which is getting more and more popular as it can not only help to finance the government's road infrastructure but also it can play a crucial role in pollution reduction and congestion management. As most of the traditional ETC schemes (ETCS) require identifying their users, they enable location tracking. This violates user privacy and poses challenges regarding the compliance of such systems with privacy regulations such as the EU General Data Protection Regulation (GDPR). So far, several privacy-preserving ETC schemes have been proposed. To the best of our knowledge, this is the first survey that systematically reviews and compares various characteristics of these schemes, including components, technologies, security properties, privacy properties, and attacks on ETCS. This survey first categorizes the ETCS based on two technologies, GNSS and DSRC. Then under these categories, the schemes are classified based on whether they provide formal proof of security and support security analysis. We also demonstrate which schemes specifically are/are not resistant to collusion and physical attacks. Then, based on these classifications, several limitations and shortcomings in privacy-preserving ETCS are revealed. Finally, we identify several directions for future research. [less ▲]

A Survey on Machine Learning-based Misbehavior Detection Systems for 5G and Beyond Vehicular Networks

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 ▲]

Federated Learning-based Inter-slice Attack Detection for 5G-V2X Sliced Networks

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 ▲]

Preventing Frame Fingerprinting in Controller Area Network Through Traffic Mutation

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 ▲]

Transforming IoT Data Preprocessing: A Holistic, Normalized and Distributed Approach

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 ▲]

CANMatch: A Fully Automated Tool for CAN Bus Reverse Engineering based on Frame Matching

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 ▲]

A Near-Field-based TPMS Solution for Heavy Commercial Vehicle Environement

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

Evaluation of TPMS Signal Propagation in a Heavy Commercial Vehicle Environement

E-print/Working paper (2021)