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International audienceAdvances in wireless communications and microelectronics have spearheaded the development of unmanned aerial vehicles (UAVs), which can be used to augment a ground network composed of sensors and/or vehicles in order to increase coverage, enhance the end-to-end delay, and improve data processing. While UAV-aided networks can potentially find applications in many areas, a number of issues, particularly security, have not been readily addressed. The intrusion detection system is the most commonly used technique to detect attackers. In this paper, we focus on addressing two main issues within the context of intrusion detection and attacker ejection in UAV-aided networks, namely, activation of the intrusion monitoring process and attacker ejection. In fact, when a large number of nodes activate their monitoring processes, the incurred overhead can be substantial and, as a consequence, degrades the network performance. Therefore, a tradeoff between the intrusion detection rate and overhead is considered in this work. It is not always the best strategy to eject a node immediately when it exhibits a bad sign of malicious activities since this sign could be provisional (the node may switch to a normal behavior in the future) or be simply due to noise or unreliable communications. Thus, a dilemma between detection and false positive rates is taken into account in this paper. We propose to address these two security issues by a Bayesian game model in order to accurately detect attacks (i.e., high detection and low false positive rates) with a low overhead. Simulation results have demonstrated that our proposed security game framework does achieve reliable detection

Recently, solutions based on Mobile Edge Computing (MEC) paradigm have been widely discussed in academia and industry. This paradigm offers solutions to address limitations, in terms of battery lifetime and processing power, of mobile and constrained devices. Despite the ever-increasing capabilities of these devices, resource requirements of applications can often transcend what is available within a single device. Offloading intensive computation tasks to a distant server can help applications reach their desired performances. In this work, we tackle the problem of offloading heavy computation tasks of Unmanned Aerial Vehicles (UAVs) while achieving the best possible tradeoff between energy consumption, time delay and computation cost. We focus on a scenario of a fleet of small UAVs performing an exploration mission. During their mission, these constrained devices have to carry-out highly intensive computation tasks such as pattern recognition and video preprocessing. We formulate the problem using a non-cooperative theoretical game with N players and three pure strategies. We provide a comprehensive proof for the existence of a Nash Equilibrium and implement accordingly a distributed algorithm that converges to such an equilibrium. Extensive simulations are performed in order to provide thorough results and assess the performances of the approach compared to three other models. Results show that our algorithm outperforms all the three approaches. Our approach achieved in average about 19%, 58% and 55% better results compared to local computing, offloading to the Edge Server (ES) and offloading to Base Station (BS) respectively.

International audienceUnmanned aerial vehicles (UAVs) networks have not yet received considerable research attention. Specifically, security issues are a major concern because such networks, which carry vital information, are prone to various attacks. In this paper, we design and implement a novel intrusion detection and response scheme, which operates at the UAV and ground station levels, to detect malicious anomalies that threaten the network. In this scheme, a set of detection and response techniques are proposed to monitor the UAV behaviors and categorize them into the appropriate list (normal, abnormal, suspect, and malicious) according to the detected cyber-attack. We focus on the most lethal cyber-attacks that can target an UAV network, namely, false information dissemination, GPS spoofing, jamming, and black hole and gray hole attacks. Extensive simulations confirm that the proposed scheme performs well in terms of attack detection even with a large number of UAVs and attackers since it exhibits a high detection rate, a low number of false positives, and prompt detection with a low communication overhead