Journal article - peer reviewed (4)
BEN MOSBAH Aziza, HAMMAMI Seif Eddine, MOUNGLA Hassine, AFIFI Hossam, KAMAL Ahmed E.
Enhancing Device-to-Device direct discovery based on predicted user density patterns. Computer networks - The international journal of computer and telecommunications networking, march 2019, vol. 151, pp. 245-259
abstract
Device-to-Device (D2D) direct discovery service is a key component for Proximity Services (ProSe) and D2D communications. Depending on the type of the studied network (pedestrian, vehicular, residential, industrial), large spatio-temporal fluctuation in mobile users' density may occur inducing several patterns throughout the day. The current standards only account for fixed configurations of this service, and currently, the research into adaptive algorithms is done using analytical models and synthetic scenarios and configurations, which makes such solutions perform poorly on real systems. We propose an adaptive D2D discovery algorithm that, building upon existing work on user density prediction analytical models of the discovery process, uses historic network traces to update its operational parameters dynamically. We test the proposed algorithm and compare it to the discovery mechanism, defined in the Third Generation Partnership Project (3GPP) standards, in order to analyze the feasibility of these types of solutions. The simulation results show that the proposed algorithm strikes a balance between network utilization and time required for discovery, which is a very promising starting point for further research on this type of solutions
KAANICHE Nesrine, LAURENT Maryline, CANARD Sebastien
Cooperative set homomorphic proofs for data possession checking in clouds. IEEE transactions on cloud computing, 2019, pp. 1-16 (document in press - published online 13 Aug 2018)
abstract
Outsourcing an increasing amount of data to a third party raises a number of security and privacy challenges, namely remote data integrity verification. Indeed, proofs for data possession checking address the verification that some previously outsourced data blocks across multiple storing nodes are correctly stored and fully available. In this paper, we propose a new set homomorphic proof of data possession, referred to as SHoPS, supporting several operations like aggregation of proofs. SHoPS is a deterministic Proof of Data Possession (PDP) scheme, based on an interactive proof protocol. Our approach has several advantages. First, it enables several proofs to be aggregated and a subset of data files' proofs to be verified, while providing an attractive communication overhead. Second, it supports public verifiability where the verification process can be delegated to another entity, thus releasing the data owner from the cumbersome task of periodical verifications. Third, SHoPS is efficient and provably secure, as it is resistant to the fraudulence of the prover and the leakage of verified data. Finally, a theoretical performances' analysis shows that SHoPS performs better in terms of functionality, communication and computation overhead compared to closely related works and experimental results point out the applicability of the proposed scheme in real world scenarios
KHODABANDELOU Ghazaleh, GAUTHIER Vincent, FIORE Marco, EL YACOUBI Mounim
Estimation of static and dynamic urban populations with mobile network metadata. IEEE transactions on mobile computing, 2019, pp. 1-14 (document in press - published online 19 Sep 2018)
abstract
Communication-enabled devices routinely carried by individuals have become pervasive, opening unprecedented opportunities for collecting digital metadata about the mobility of large populations. In this paper, we propose a novel methodology for the estimation of people density at metropolitan scales, using subscriber presence metadata collected by a mobile operator. Our approach suits the estimation of static population densities, i.e., of the distribution of dwelling units per urban area contained in traditional censuses. More importantly, it enables the estimation of dynamic population densities, i.e., the time-varying distributions of people in a conurbation. By leveraging substantial real-world mobile network metadata and ground-truth information, we demonstrate that the accuracy of our solution is superior to that granted by state-of-the-art methods in practical heterogeneous urban scenarios
KIENNERT Christophe, ZIAD Ismail, DEBAR Hervé, LENEUTRE Jean
A survey on game-theoretic approaches for intrusion detection and response optimization. ACM computing surveys, january 2019, vol. 51, n° 5, pp. article 90
abstract
Intrusion Detection Systems (IDS) are key components for securing critical infrastructures, capable of detecting malicious activities on networks or hosts. However, the efficiency of an IDS depends primarily on both its configuration and its precision. The large amount of network traffic that needs to be analyzed, in addition to the increase in attacks' sophistication, renders the optimization of intrusion detection an important requirement for infrastructure security, and a very active research subject. In the state of the art, a number of approaches have been proposed to improve the efficiency of intrusion detection and response systems. In this article, we review the works relying on decision-making techniques focused on game theory and Markov decision processes to analyze the interactions between the attacker and the defender, and classify them according to the type of the optimization problem they address. While these works provide valuable insights for decision-making, we discuss the limitations of these solutions as a whole, in particular regarding the hypotheses in the models and the validation methods. We also propose future research directions to improve the integration of game-theoretic approaches into IDS optimization techniques
Communication in a conference - peer reviewed (1)
BARBEAU Michel, GARCIA-ALFARO Joaquin, KRANAKIS Evangelos
Geocaching-inspired resilient path planning for drone swarms. MISARN 2019: Mission-Oriented Wireless Sensor, UAV and Robot Networking, Los Alamitos : IEEE Computer Society, 29-29 april 2019, Paris, France, 2019, pp. 1-6 (document in press)
URL: http://www-public.imtbs-tsp.eu/~garcia_a/web/papers/preprint-misarn2019.pdf
abstract
A path planning algorithm for drone swarms is presented. From the outset, none of the drones knows the path and final destination. Together, they collectively determine and unravel step-by-step the waypoints and final destination, resolving a localization problem at each step. It is a shared-information path planning algorithm. The algorithm is fault-tolerant and resilient to drones falling victim of attacks to their positioning system. It is shown that correctly functioning drones navigate the path provided that the number of faulty drones is less than $\frac{n-d}{2}$, where $n$ is the total number of drones and $d$, equal to two or three, is the dimension of the space navigated by the drones. We validate the algorithm with appropriate simulations, implemented over OMNeT++ and GNSSim, which allow building network simulations including GPS attacks (e.g., jamming and spoofing attacks). The OMNeT++ models and the GNSSim functions are linked together, to validate our work
Last modify October 1, 2012
