The one-time pad is an encryption technique that is proven to be unbreakable by an eavesdropper. It relies on a single-use secret key shared between a sender and a receiver, whose length is that of the message to be encrypted. The creation of such a key can be achieved through Quantum Key Distribution (QKD), a method based on quantum physics that ensures eavesdroppers' actions can be statistically detected with arbitrarily high probability. It requires a quantum channel for qubit transmission and a classical channel for classical bit transmission. However, the classical channel is generally authenticated by classical means using cryptographic hash functions that are not provably secure. In this paper, we propose for the first time a QKD protocol, based on BB84, that uses quantum error estimation to authenticate both channels. This method relies on the consumption of a smaller pre-shared key, and is provably secure thanks to fundamental results in both information theory and quantum physics.

Detecting faults in distributed network systems is challenging because of their complexity, but this is required to evaluate and improve their reliability. This paper proposes ChaT, a testing and evaluation methodology under system reconfigurations and perturbations for distributed network systems, to evaluate QoS reliability by discriminating safe and failure-prone behaviors from different testing scenarios. Motivated by meta-morphic testing technique that removes the burden of defining software oracles, we propose some metamorphic relationships that correlate system inputs and outputs to find patterns in executions. Classification techniques based on machine learning (principal component analysis and support vector machine) are used to identify system states and validate the proposed metamorphic relationships. These metamorphic relationships are also used to help anomaly detection. We verify this with several anomaly detection techniques (isolation forest, one-class SVM, local outlier factor, and robust covariance) that categorize experiments belonging to either safe or failure-prone states. We apply ChaT to a video streaming application use case. The simulation results show the effectiveness of ChaT to achieve our goals: identifying execution classes and detecting failure-prone experiments based on metamorphic relationships with high level of statistical scores.

A wide diversity of IoT devices are connected in various smart homes with an extremely rapid growth. Identifying IoT devices as they connect to the network enables better devices and services management. However, autonomous identification is very challenging in such heterogeneous environments. In this paper, we present a near-real time classification approach, based on network features that are extracted both from characteristics of traffic flows and from the payloads of the packets, to discriminate device types. Our solution automatically identifies a newly connected device to the home network. Furthermore, we evaluate the performance of our method using a representative and heterogeneous set of real IoT devices. Our results show autonomous recognition with 97% average accuracy, based on decision tree models using the one-vs.-all method.

We present SVVAMP, a Python package dedicated to the study of voting systems with an emphasis on manipulation analysis.

We investigate the possibility of designing a voting rule that both meets the Condorcet criterion and has a low vulnerability to coalitional manipulation. For this purpose, we examine the Condorcification of a voting rule, where the original rule is altered to elect the Condorcet winner when one exists, and we study its impact on coalitional manipulability. A recent result states that for a large class of voting rules based on strict total orders, their Condorcification is at most as coalitionally manipulable as the original rule. We show that for most of them, the improvement is strict. We extend these results to a broader framework that includes weak orders and cardinal voting rules. These results support the main message of this paper: when searching for a “reasonable” voting rule with minimal coalitional manipulability, investigations can be restricted to Condorcet rules. In other words, in a class of “reasonnable” voting rules, it is possible to have both the Condorcet criterion and a minimal vulnerability to coalitional manipulation.

The adoption of Service Oriented Architecture (SOA) and semantic Web technologies in the Internet of Things (IoT) enables to enhance the interoperability of devices by abstracting their capabilities as services and to enrich their descriptions with machine-interpretable semantics. This facilitates the discovery and composition of IoT services. The increasing number of IoT services, their dynamicity and geographical distribution require mechanisms to enable scalable and efficient discovery. We propose in this paper a semantic based IoT service discovery system that supports and adapts to the dynamicity of IoT services. The discovery is distributed over a hierarchy of semantic gateways. Within a semantic gateway, we implement mechanisms to dynamically organize its content over time, in order to minimize the discovery cost. Results show that our approach enables to maintain a scalable and efficient discovery and limits the number of updates sent to a neighboring gateway.

Internet is a large-scale and highly competitive economic ecosystem. In order to make fair decisions, while preventing the economic actors from manipulating the natural outcome of the decision process, game theory is a natural framework, and voting systems represent an interesting alternative that, to our knowledge, has not yet being considered. They allow competing entities to decide among different options. In this paper, we investigate their use for end-to-end path selection in multi-operator networks, analyzing their manipulability by tactical voting and their economic efficiency.We show that Instant Runoff Voting is much more efficient and resistant to tactical voting than the natural system which tries to get the economic optimum.

In the ever increasing Quality of Service competition, the sudden nature of failure compels network operators to invest capital (CAPEX) and operational (OPEX) expenditures in the fault-management tasks. Considering the GMPLS protocol, the recovery strategy is a tradeoff between resource consumption and availability. To solve this dilemma, we propose to dynamically adapt the GMPLS recovery level in real-time, to fit with the monitored risk of failure. For this purpose, the network equipments health is used to switch seamlessly between the restoration and protection schemes. The consequences in terms of resource consumption and availability have been estimated based on an analytical model and then simulated to measure the expected benefits of the proposed proactive self-healing function.

The growth of User-Generated Content (UGC) traffic makes the understanding of its nature a priority for network operators, content providers and equipment suppliers. In this paper, we study a four-month dataset that logs all video requests to DailyMotion made by a fixed subset of users. We were able to infer user sessions from raw data, to propose a Markovian model of these sessions, and to study video popularity and its evolution over time. The presented results are a first step for synthesizing an artificial (but realistic) traffic that could be used in simulations or experimental testbeds.

Many methods have been proposed in the literature to perform admission control in order to provide a sufficient level of Quality of Service (QoS) to accepted flows. In this paper, we introduce a novel data-driven method based on a time-varying model that we refer to as Knowledge-Based Admission Control solution (KBAC). Our KBAC solution consists of three main stages: (i) collect measurements on the on-going traffic over the communication link; (ii) maintain an up-to-date broad view of the link behavior, and feed it to a Knowledge Plane; (iii) model the observed link behavior by a mono-server queue whose parameters are set automatically and which predicts the expected QoS if a flow requesting admission were to be accepted. Our KBAC solution provides a probabilistic guarantee whose admission threshold is either expressed, as a bounded delay or as a bounded loss rate. We run extensive simulations to assess the behavior of our KBAC solution in the case of a delay threshold. The results show that our KBAC solution leads to a good trade-off between flow performance and resource utilization. This ability stems from the quick and automatic adjustment of its admission policy according to the actual variations on the traffic conditions.

Admission control is a mechanism used to restrict access to a computer network to some flows based on the current utilization level of the network resource. By regulating the number of on-going flows, admission control aims at preventing over loading, congestion and performance collapses, so that, accepted flows receive a sufficient level of Quality of Service (QoS). In this paper, we evaluate three existing measurement-based admission control (MBAC) solutions, and we compare their efficiency in the context of semantic networks. Semantic networks refer to networks that autonomously acquire a knowledge on the on-going traffic as well as on any new incoming flow requesting admission. In this framework, we configure the three MBAC solutions in a way they have an identical target in terms of maximum tolerable packet loss rate or maximum tolerable packet queueing delay. We evaluate the solutions performance analytically or by simulation, and compare them to the "ideal" admission control. The results show that one solution, outperforms the others in meeting the target performance.

We describe the dimensioning and benchmarking of packet transport networks with aggregated link capacities up to the Tbit/s range, using a circuit-switching (cross-connect) infrastructure below packet-switching nodes. The different network solutions to be evaluated are designed to carry large numbers of high-definition video and other data streams, with upper bounds on packet latency and packet loss rate for all flows. Trends of evolution are analyzed, based on a performance and cost comparison of several node architectures with different associated transport technologies (packet and circuit layers). Simulations of packet-switch nodes under pseudo-self-similar traffic, extraction of analytical models for latency and loss, end-to-end best path selection, and constrained optimization of crossconnect and packet-switch resources are combined to derive the dimensioning of all the network elements. As a result of this study, photonic cross-connects associated with carrier-grade Ethernet switches appear as the most efficient architectures, thanks to cost savings for transit traffic. Single-layer solutions involving for example only packet-switch routers turn out to be more expensive.