Security is a major challenge in swarm of drones. Network intrusion detection systems (IDS) are deployed to analyze and detect suspicious packet flows. Traditionally, they are implemented independently on each drone. However, due to heterogeneity and resource limitations of drones, IDS algorithms can fall short in satisfying Quality of Service (Qo$S$) metrics, such as latency and accuracy. We argue that a drone can make profit from the swarm by delegating part of the analysis of their packet flows to neighbor drones that have more processing power to enforce security. In this paper, we propose two solving methods to distribute the packet flows to analyze among drones in a way to ensure that it is processed with a minimum communication overhead to limit the attack surface, while ensuring Qo$S$metrics imposed by the drone mission. First, we propose a formulation of the distribution problem using both an Integer Linear Programming (ILP) and a Maximum-Flow Minimum-Cost (MFMC). Furthermore, we propose two specific solving methods for the distribution problem: (1) a Greedy Heuristic (GH), a non-exact solving method, but with small time overhead, and (2) an Adapted Edmonds-Karp (AEK) algorithm, an exact method, but with a higher time overhead. GH proved to be a very fast solution (up to more than 2000x faster than ILP with Branch and Bound), while AEK solution proved to find the exact solution even when the problem is very difficult.

Drone swarms are increasingly being used to perform critical missions, such as inspection of ports and industrial installations. Each drone can embed heterogeneous execution platforms to successfully perform various computing tasks. As security threats may disrupt the progression of the drone mission, network intrusion detection systems (IDSs) are used. They analyze network traffic to detect malicious behaviors, but generally rely on resource-hungry machine learning models. To adapt to the dynamic nature of the mission, it is necessary to embed several IDS implementations leveraging heterogeneous computing resources of the drone and presenting a trade-off between security, throughput, and energy consumption. To address this issue, we propose, in this paper, an end-to-end flow composed of an offline phase to choose the IDS implementations to embed on the drone platform and an online phase to select the best implementation online considering the mission conditions at a given time. We devised a MILP formulation for the offline phase that proved to provide a 89.41% better Inverted Generational Distance (IGD) than a random choice. For the online phase, we investigated several solutions and designed a novel optimized strategy that proved to be around 16.76 times faster than TOPSIS while having comparable QoS metrics.

Swarms of drones are more and more used for critical missions and need to be protected against malicious users. Intrusion Detection Systems (IDS) are used to analyze network traffic in order to detect possible threats. Modern IDSs rely on machine learning models for such a sake. Because of the absence of central management in swarms of drones, IDSs constitute a good second-line protective measure. Investigating the execution of IDS (resource-hungry) algorithms on drone (resource-constrained) devices is crucial when it comes to optimizing energy, response time, memory footprint and algorithm precision. In addition, embedded platforms used in drones often incorporate heterogeneous computing platforms on which IDSs could be executed. In this paper, we present a methodology and results about characterizing the execution of different IDS models on various platform (CPUs, GPUs). In effect, as swarm of drones operate in different mission contexts (e.g. criticity level) and states (e.g. energy budget, memory footprint), it is important to explore which IDS model to run on which platforms for a given mission in a given context. For this sake, we evaluated several metrics on different platforms: energy and resource consumption, accuracy for malicious traffic detection and response time. The models tested (RF, CNN, DNN) have shown different performance according to the measured metrics and the chosen platform and proved to be relevant in different mission states.

The next generation of mobile systems based on LTE-A (Long Term Evolution-Advanced) networks are expected to support the new promising technology M2M (Machine-to-Machine) communications while keeping an eye on its previous H2H (Human-to-Human) communications not to be affected especially when it comes to the expected exponential growth of the number of M2M in the coming years, in particular, with the advance of IoT (Internet of Things) deployment and the expected ubiquity of such objects in the near future. In this article, we review the M2M communication technology from the LTE-A perspective and we outline the random access challenges in high dense areas where the LTE-A network is striving to fulfill the massive number of M2M devices. Moreover, we compare the most common mechanisms found in the literature that deal with the RACH (Random Access Channel) procedure issues and challenges by analyzing the existing solutions and approaches to avoid RACH overload congestion in the M2M communications. To this end, we have developed different M2M scenarios using SimuLTE Modeler to investigate the impact of M2M communications on LTE-A networks in emergency events.

Nowadays, many wireless communication devices should share the precious radio-frequency bandwidths. In order to get the most of the available spectrum, Cognitive Radio (CR) are recently introduced to allow better spectrum management for newly standardized radio communication systems. An advanced multistandard terminal device may cover several communication standards. The system of such device should be highly flexible to dynamically and autonomously adjust its radio operating parameters. Moreover, the reduction of interference among radio communication systems is needed to allow the coexistence of different standards in the same device. In this paper, a new concept and system architecture to reach our goal are proposed and discussed. Our ultimate goal aims to ensure the coexistence of different standards in a CR mobile terminal.