International audience

Cooperative vehicular platoons improve road safety and reduce congestion through synchronized maneuvers enabled by Vehicle-to-Vehicle (V2V) communication. Vehicles are authenticated using certificates from the Cooperative Intelligent Transport Systems (C-ITS) Public Key Infrastructure (PKI). Short-term certificates, serving as vehicle identifiers, change over time and distance, which may lead to legitimate members being misidentified as attackers in the platoon, resulting in false positives. Additionally, sensitive data, such as platoon IDs, must be protected from impersonation attacks by external vehicles. To address these challenges, we propose a secure group key-based authentication framework that uses post-quantum cryptography and Shamir's Secret Sharing for key exchange. This ensures accurate member authentication and protection of platoon data. Security analysis using the Scyther tool along with simulations using PLEXE simulator demonstrate the protocol's effectiveness in securing platoon operations against cyber threats.

With the increase in cyberattacks, developing appropriate strategies to mitigate and prevent them is essential. In the literature, tools exist that either help prevent or mitigate them. Attack graphs help define mitigation strategies because they help represent and visualize the attacker’s position on a system. However, the mitigation actions are not instantiated on the attack graph. This paper proposes an approach to generate an automated attack-defense graph based on real-time monitored system alerts and an extensive and comprehensive state-of-the-art review. We propose to enrich logical attack graphs generated by a logical reasoner. The enrichment process is possible thanks to a vulnerability ontology that infers additional impacts for an exploited vulnerability. We propose a countermeasure selection approach based on graph matching to generate an optimal Incident Response (IR) playbook. We propose instantiating the generated playbook’s IR actions to get an attack-defense graph in real-time. This instantiation is done thanks to anti-correlation. The anti-correlation ensures that the countermeasures are instantiated on the appropriate attack graph nodes. Only the IR actions whose execution can be launched automatically are applied. We validate our approach using two use-case scenarios that target critical industrial infrastructures. We analyze the countermeasures instantiated on the attack graphs for the scenarios that can achieve the attack goal. We evaluated the approach concerning the security relevance of instantiated countermeasures in attack graphs for several attack paths. The countermeasures instantiated on a node are always relevant to the attacker’s action represented by this node. We also evaluate the approach regarding time performance, considering several situations for the use-case scenarios. The generation time depends on the number of vulnerabilities involved in the scenario. The generation time is on average 0.161 s when the playbook has been generated before the attack defense graph generation process.

Cyber-attacks are increasing; it is more urgent for organizations to automate their Incident Response (IR) plan process. Attack Graphs (AGs) are used to represent actions followed by an adversary to reach a goal. However, an expert should analyze each possible action and their impact to decide which mitigation actions should be applied to block the attack fulfillment. This paper proposes an approach to generate Attack-Defense Graphs in real-time by instantiating IR actions on a logical AG. The system’s real-time monitoring enables the detection of malicious actions, which leads to the generation of alerts mapped with the AG to deduce the attacker’s location on the system. Our solution can decide where to apply IR actions to mitigate the attack impact. These IR actions are part of playbooks that are generated automatically for the attack. We propose correlating IR actions with the AG fact nodes to choose which IR actions to instantiate on the AG. Therefore, we propose generating predicates for the mitigation actions, which are mapped with the AG predicates. We validate our approach with an industrial use case. An asset is vulnerable to Remote Code Execution (RCE) requiring user credentials that can be obtained through a brute force attack. We show how our approach helps anticipate an adversary’s next step. The countermeasures predicates are instantiated on the AG to prevent the attacker from going further on the system.

Collective Perception Services (CPS) enable communicating entities to share their perception data in the V2X communication network. Potential attacks on extended perception data affect the CPS and may consequently degrade the safety application that rely on collective perception data. In this paper, we build an architecture that allows the integration of misbehavior detection and mitigation mechanisms with the CPS. We implement the Intersection Movement Assist (IMA) application that uses the extended perception data to calculate potential collision risks in intersection areas. We define specific safety metrics and through extensive simulations in large scale scenarios, we quantify the impact of a large number of attacks and of misbehavior detection on the safety application. Our evaluation demonstrates the ability of misbehavior detection and mitigation mechanisms to filter malicious shard perception data and consequently the benefits of using such mechanisms in improving the robustness of the safety application in complex road scenarios.

Cloud-RAN is a key 5G-enabler; it consists in centralizing the baseband processing of base stations by executing the baseband functions in a centralized, virtualized, and shared entity known as the Base Band Unit (BBU)-Pool. Cloud-RAN paves the way for joint management of the resources of multiple base stations. This paper aims to analyze the potential reduction in power consumption brought by the joint allocation of the radio and computing resources. We formulate a Mixed Integer Linear Programming (MILP) problem, considering the objective of power consumption minimization. For comparison, we consider the objective of throughput maximization. When the goal is power minimization, the joint allocation can minimize the total power consumption by up to 21.2%, with respect to the case where radio and computing resources in the BBU pool are allocated sequentially.

The principal tenet of C-RAN is the softwarization of the base-band signal processing, which enables the sharing of computing resources among multiple radio heads. When the aggregate demand exceeds the processing capacity, a fraction of the radio packets is lost at PHY layer. Traditional computing resource allocation policies aim to minimize the packet loss rate. Dropping a PHY packet triggers a retransmission, unless the lost packet corresponds to the last available HARQ round, in which case the entirety of the radio resources spent on the multiple transmissions go to waste. This suggests that allocating computing resource accounting also for the HARQ transmission history may make a more efficient use of the bandwidth. We consider a simplified LTE uplink setting, and we measure the performance at the lower MAC layer (accuracy, goodput and average delay). We first compare the PHY-layer loss rate minimization and the cross-layer approaches using an ILP formulation. The cross-layer approach brings a tangible improvement, especially in accuracy. This suggests, for future work, that joint radio and computing resource allocation may further enhance spectral efficiency. We finally propose a probabilistic algorithm amenable to real-time operation which allows to mix strategies via parameter tuning, and we use it to explore the region of achievable goodput/accuracy trade-offs.

In order to improve the transmission reliability in current wireless communication systems, the Hybrid Automatic ReQuest (HARQ) protocol is employed to manage the unknown time-varying channel. The acknowledgments are fed back with delay on the return link. To fill up the idle time between a transmission and its acknowledgment, parallel HARQ streams associated with different messages are carried out. In this paper we improve on parallel HARQ by proposing a multi-layer HARQ protocol (also called superposition coding or multi-packet HARQ), where a single transmission may carry information on multiple messages. The multi-layer HARQ protocol works in presence of delay on the return link as parallel HARQ does, and does not require additional feedback such as the channel state information. It aims at improving the accuracy as well as the user's delay distribution, thus achieving throughput increase. Assuming capacity-achieving codes, we show that the proposed protocol outperforms parallel HARQ in throughput, message error rate, and delay distribution. Using practical codes and decoding algorithms the gains are as well significant, at the expense of the receiver's complexity.

This paper considers wireless communication protocols implementing both cooperation and hybrid automatic repeat request. Based on a simple example (one source, one relay, and one destination), we propose the use of probabilistic protocols as an alternative to classical, deterministic ones. Starting initially with the finite state Markov chain (FSMC) analysis of a deterministic protocol, the idea of probabilistic protocol comes as an association with the simplified FSMC of the deterministic protocol. This FSMC contains two parameters that can be optimized for finding the best performance. So far, probabilistic protocols have been proposed at higher layers of communication systems, while we consider here the physical and MAC layers. In our analysis, the relay works in the demodulate-and-forward mode, and we demonstrate that: 1) the FSMC analysis predicts accurately the performance of the actual system and 2) the performance of the probabilistic protocol (when the two parameters are carefully tuned) outperforms the performance of a reference deterministic protocol. The analysis is checked upon Monte Carlo simulations.

In recent years we are witnessing an increasing interest of young players in Multiplayer Online Battle Arenas (MOBAs). In a MOBA, two or more teams compete for resources on a limited map with real-time interactions. In order to achieve the required level of coordination, network performances must offer to each player an adequate and uniform Round Trip Time (RTT) to the game server. Unfortunately, different ISPs will give different RTTs based on technical and commercial settings. In this paper, we define an overlay network to convey all the team traffic through the teammate with the lowest end-to-end delay to the game server. By doing so, we leverage on the peering between ISPs, usually offering better performances due to commercial agreements, to obtain an homogeneous and bounded delay for all team members. The proposed solution has been tested using the popular game League of Legends. In this paper preliminary, and promising, results are presented.

In current wireless communication systems, the feedback required by the Hybrid Automatic ReQuest (HARQ) mechanism is received with some delay at the transmitter side. To alleviate this issue, parallal Stop-and-Wait HARQ is usually employed. In this paper, we propose a multi-packet HARQ protocol (also called superposition coding or multi-layer HARQ) to improve the user's delay distribution and increase the throughput, without any additional feedback such as Channel State Information. The performance analysis, provided from an information-theoretic point-of-view, shows that the proposed protocol offers better delay distribution, higher throughput and lower message error rate compared to the conventional parallel Stop-and-Wait HARQ, at the expense of increased decoding complexity.

Propagation of faulty data is a critical issue. In case of Delay Tolerant Networks (DTN) in particular, the rare meeting events require that nodes are efficient in propagating only correct information. For that purpose, mechanisms to rapidly identify possible faulty nodes should be developed. Distributed faulty node detection has been addressed in the literature in the context of sensor and vehicular networks, but already proposed solutions suffer from long delays in identifying and isolating nodes producing faulty data. This is unsuitable to DTNs where nodes meet only rarely. This paper proposes a fully distributed and easily implementable approach to allow each DTN node to rapidly identify whether its sensors are producing faulty data. The dynamical behavior of the proposed algorithm is approximated by some continuous-time state equations, whose equilibrium is characterized. The presence of misbehaving nodes, trying to perturb the faulty node detection process, is also taken into account. Detection and false alarm rates are estimated by comparing both theoretical and simulation results. Numerical results assess the effectiveness of the proposed solution and can be used to give guidelines for the algorithm design.

Getting confidence regions for parameter estimates obtained from data collected by a wireless sensor network (WSN) is very important to assess the performance of the estimator. The sign perturbed sums (SPS) approach has been proposed recently to defined exact confidence regions in a centralized setting even if only few measurements are available. SPS may be distributed to get confidence regions at each node of a WSN. This paper investigates a data dissemination strategy called Tagged and Aggregated Sums (TAS), exploiting the particularities of SPS, to efficiently provide each node with the information necessary to evaluate locally the confidence region. TAS and flooding (FL) algorithms have been investigated through simulations and then implemented on commercial sensor nodes. The impact of collision avoidance mechanisms at the medium access control (MAC) layer is also experimentally assessed. Performance comparisons show that TAS outperforms FL in structured networks.1

This paper considers a delay tolerant network consisting of nodes equipped with sensors, some of them producing outliers. A distributed faulty node detection (DFD) algorithm, whose aim is to help each node in estimating the status of its sensors, has been proposed recently by the authors. The aim of this paper is to analyze the robustness of the DFD algorithm to the presence of misbehaving nodes performing Byzantine attacks. Two types of attacks are considered and analyzed, each trying to mislead the other nodes in the estimation of the status of their sensors. This provides insights on the way the parameters of the DFD algorithm should be adapted to minimize the impact of misbehaving nodes. Theoretical results are illustrated with simulations considering nodes with random displacements, as well as traces of node inter-contact times from real databases.

Relaying protocols are usually deterministic, i.e. they commute from one action to the next one in a predefined manner, depending on the success of the transmission. This paper demonstrates the advantages that probabilistic protocols (i.e. the next action to be undertaken depends on some probability) may have in this context. The whole study is performed on a simple source-relay-destination network, the relay working in demodulate and forward mode, in order to obtain insights on the interaction between relaying and HARQ. So far, probabilistic protocols have been mainly proposed for higher layers of communication systems, but are applied here to physical and MAC layers. Since it contains only two parameters, we demonstrate that our probabilistic protocol can easily be tuned for best performance using a Finite State Markov Chain (FSMC), and that it brings improvement over deterministic protocols in the same scenario.

This work analyses the impact of channel access issues and packet losses on a distributed defective sensor detection algorithm. A theoretical analysis is performed to characterize the detection performance. Matlab simulation results for the detection algorithm are then provided. Finally, experimental results conducted on a real wireless sensor network involving the IEEE 802.15.4 standard are reported.

Due to their inherent feature of exhibiting frequent disconnections, propagation of faulty data in Delay Tolerant Networks can be a critical aspect to counteract. Indeed the rare meeting events require that nodes are effective and efficient in propagating the correct information. Accordingly mechanisms to rapidly identify possible faulty or misbehaving nodes should be searched. Distributed fault detection has been addressed in the literature in the context of sensor and vehicular networks, but unfortunately these solutions suffer for long delays in identifying and isolating misbehaving nodes. In this paper instead we propose a fully distributed, easily implementable, and fast convergent approach to allow each DTN node to rapidly identify whether its sensors are producing outliers. The behavior of the proposed algorithm is described by some continuous-time state equation, whose equilibrium is characterized. Detection and false alarm rates are estimated by comparing both theoretical and simulation results. Numerical results assess the effectiveness of the proposed solution and can give guidelines in the design of the algorithm.

This paper addresses the information theoretical analysis of data compression achieved by random linear network coding in wireless sensor networks. A sparse network coding matrix is considered with columns having possibly different sparsity factors. For stationary and ergodic sources, necessary and sufficient conditions are provided on the number of required measurements to achieve asymptotically vanishing reconstruction error. To ensure the asymptotically optimal compression ratio, the sparsity factor can be arbitrary close to zero in absence of additive noise. In presence of noise, a sufficient condition on the sparsity of the coding matrix is also proposed.

To guarantee its integrity, a wireless sensor network needs to efficiently detect faulty nodes producing erroneous measurements. This paper proposes a fully distributed fault detection algorithm. A node first collects the measurements of its neighborhood, processes them to decide whether they contain outliers, and broadcasts the result. Then, it decides autonomously about its functioning status. The detection algorithm is proposed in two variants, depending on the proportion of faulty nodes in the network. A theoretical analysis of the probability of error and of the convergence of the algorithm is provided. The tradeoff between false alarm probability and detection probability is characterized using simulation.

The objective of this paper is the characterization of the Wyner-Ziv rate-distortion function for memoryless continuous sources, when the correlation between the sources is modeled via an additive noise channel. Modeling the distribution of the correlation noise via a Gaussian mixture, with discrete or continuous mixing variable, provides a unified signal model able to describe a wide class of distributions, useful in the context of practical applications. The Wyner-Ziv rate-distortion function associated with this signal model cannot, in general, be obtained in analytical form. This paper contributes a method for its analysis, by providing computable upper and lower bounds.

Cross-layer error protection strategies couple systematic error correction at the physical layer with adaptive data retransmission at the data-link layer, to mitigate the effects of fast-fading propagation channels. This work, in the form of a case-study, aims to introduce useful tools for the assessment and analysis of the energetic performance of such strategies. In particular, it is shown that in energy-constrained applications, such as wireless sensor networks, the design guidelines should be based on a precise definition of the energy per bit. This definition also allows to evaluate the minimum energy that is necessary to transmit data in a given setting.

This paper focuses on the performance of a Wyner-Ziv coding scheme for which the correlation between the source and the side information is modeled by a hidden Markov model with Gaussian emission. Such a signal model takes the memory of the correlation into account and is hence able to describe the bursty nature of the correlation between sources in applications such as sensor networks, video coding etc. This paper provides bounds on the rate-distortion performance of a Wyner-Ziv coding scheme for such model. It proposes a practical coding scheme able to exploit the memory in the correlation. Finally, the contribution to each part of the coding and decoding scheme is analysed.

This work addresses the problem of correlation detection in a group of elliptically-contoured variables, when the number p of variates greatly exceeds the number n of observed samples. We exploit the properties inherent to the Z-score representation of the data set to devise two different decision tests, whose performances are assessed by upper bounding the Type I and Type II error probabilities. The results specifically apply to the asymptotic regime where the number of variates p is large, and the number of samples n is finite and fixed.

This paper presents a practical coding scheme for the direct symmetric multiterminal source coding problem with remote source, when the noise between the remote source and the observations is Gaussian-Bernoulli-Gaussian. The idea behind the design is to take advantage from the observed symbols being in the real field, in order to perform low-dimensional compressed sensing. The coding scheme is based on channel coding techniques involving BCH codes over the real field. Simulations with respect to the rate distortion performance are provided, along with insights about optimization issues. Robustness to variations of the probability of impulse is also investigated. Perspectives about the application to the CEO problem in presence of Gaussian-Bernoulli-Gaussian correlation noise between the remote source and the observations conclude the paper.

Practical schemes for distributed video coding with side information at the decoder need to consider non-standard correlation models in order to take non-stationarities into account. In this paper we introduce two correlation models for Gaussian sources, the Gaussian- Bernoulli-Gaussian (GBG) and the Gaussian-Erasure (GE) models, and evaluate lower and upper bounds on their rate-distortion functions. Provided that the probability of impulse noise or of erasures remains small, these bounds remain close to the rate-distortion function for Gaussian correlation. Two practical schemes for the GE correlation model are also presented, with performance about 1.5 dB away from the lower bound.