The Internet of Things (IoT) has continued gaining in popularity and importance in everyday life in recent years. However, this development does not only present advantages. Indeed, due to the number of sensitive and private data produced by IoT systems, they have become the new privileged targets for cyberattackers. At the same time, Machine Learning (ML) has gained a phenomenal success in various fields like telecommunications, transport or cybersecurity. Nonetheless, the application of ML can cause significant damage when put in the hands of an attacker. Contrary to many previous works, we do not focus on the potential contributions of ML in the IoT security systems. Indeed, this survey aims to provide a comprehensive overview of ML approaches to enable more effective and less detectable attacks. Thereby, the purpose of this article is to identify and discuss the advantages of the elaboration of ML attacks and the possible solutions already evoked in the literature. Firstly, we provide an identification of the main threats and potential attacks on IoT networks. Then, we investigate on cyberattacks integrating machine learning algorithms during the last few years and we provide future research directions, especially for jamming, side channel, false data injection and adversarial machine learning attacks.

Time-synchronized channel hopping (TSCH) is currently the most efficient solution for collision-free interference-avoiding communications in ad hoc wireless networks, such as wireless sensor networks, vehicular networks, and networks of robots or drones. However, all variants of TSCH require some form of centralized coordination to maintain the time-frequency slotting mechanism. This leads to slow convergence to steady state and moderate time-frequency slot utilization, particularly under node churn or mobility. We propose decentralized time-synchronized channel swapping (DT-SCS), which is a novel protocol for medium access control (MAC) in ad hoc wireless networks. Under the proposed protocol, nodes first converge to synchronous beacon packet transmissions across all available channels at the physical layer, with a balanced number of nodes in each channel. This is done by the novel coupling of distributed synchronization and desynchronization mechanisms-which are based on the concept of pulse-coupled oscillators-at the MAC layer. Decentralized channel swapping can then take place via peer-to-peer swap requests/acknowledgments made between concurrent transmitters in neighboring channels. We benchmark the convergence and network throughput of DT-SCS, TSCH, and the efficient multichannel MAC protocol (seen as the state of the art in decentralized, interference-avoiding, and multichannel MAC protocols) under simulated packet losses at the MAC layer. Moreover, performance results via a Contiki-based deployment on TelosB motes reveal that DT-SCS comprises an excellent candidate for decentralized multichannel MAC-layer coordination by providing for quick convergence to steady state, high bandwidth utilization under interference and hidden nodes, as well as high connectivity.

Visible light communication (VLC) builds on the dual use of lightening infrastructure for communication. Even though the advantages of VLC are well known, as emerging communication paradigm, some open issues still need to be addressed in order to rely on it as a robust communication system. First of all, external interference as an extremely varying signal impacting on the reliability of the VLC system needs to be analyzed. In this article, we propose a system where the link conditions (in terms of signal-to-noise-ratio (SNR)) drive the modulation scheme and this procedure is managed through the use of an uplink/channel, to assure a feedback path. The receiver is in charge of choosing the modulation scheme matching the requirement in terms of error rate on the basis of the measured SNR after noise mitigation. The feasibility of the system and its effectiveness are evaluated by designing and implementing a complete bi-directional system. In particular, an uplink channel sending the information regarding the specific selected modulation technique has been implemented and the whole system is based on a fine synchronization approach in order to “track” in real time the most suitable modulation scheme. Experimental results show the effectiveness of a bi-directional system in order to implement an adaptive VLC system able to follow the environmental changes (in terms of interference and noise).

With the ever advancing expansion of the Internet of Things (IoT) into our everyday lives, the number of attack possibilities increases. Furthermore, with the incorporation of the IoT into Critical Infrastructure (CI) hardware and applications, the protection of not only the systems but the citizens themselves has become paramount. To do so, specialists must be able to gain a foothold in the ongoing cyber attack war-zone. By organising the various attacks against their systems, these specialists can not only gain a quick overview of what they might expect but also gain knowledge into the specifications of the attacks based on the categorisation method used. This paper presents a glimpse into the area of IoT Critical Infrastructure security as well as an overview and analysis of attack categorisation methodologies in the context of wireless IoT-based Critical Infrastructure applications. We believe this can be a guide to aid further researchers in their choice of adapted categorisation approaches. Indeed, adapting appropriated categorisation leads to a quicker attack detection, identification, and recovery. It is, thus, paramount to have a clear vision of the threat landscapes of a specific system.

In this work, we consider Reconfigurable Intelligent Meta-surfaces (RIMs) to provide effective beam steering functionality, while enhancing the coverage of the fifth generation (5G) users in a urban city mobility context. In particular, we enable communications in the millimeter wave (mmWave) frequencies, that can occur simultaneously with other 5G communications. The main objective of this paper is to demonstrate the feasibility and advantages of an integrated Computed Vision (CV) approach for tuning the meta-atom states of a RIM structure working at mmWave frequency band, without estimating the interferes' radio channel. In particular, the CV system will feed a logic unit running a Machine Learning (ML) algorithm to compute in real-time the coding schemes, namely the sequence of binary states associated to each unit-cell of the RIM, in order to obtain the target reconfigurable radiation pattern. Specifically, a Genetic Algorithm (GA) is introduced to derive the most suitable radiation pattern for Beam Steering application, based on the input of a CV infrastructure. Results show the feasibility of such a kind of system, with an higher coverage achieved in dense scenarios, by improving the robustness against the potential blockages introduced by the mmWave technology. Moreover, we demonstrate the system is robust against the inaccuracy introduced by the CV and GA.