The accelerated move towards the adoption of the industrial Internet of Things (IIoT) paradigm has resulted in numerous shortcomings as far as security is concerned. One of the IIoT affecting critical security threats is what is termed as the ” False Data Injection” (FDI) attack. The FDI attacks aim to mislead the industrial platforms by falsifying their sensor measurements. FDI attacks have successfully overcome the classical threat detection approaches. In this study, we present a novel method of FDI attack detection using Autoencoders (AEs). We exploit the sensor data correlation in time and space, which in turn can help identify the falsified data. Moreover, the falsified data are cleaned using the denoising AEs. Performance evaluation proves the success of our technique in detecting FDI attacks. It also significantly outperforms a support vector machine (SVM) based approach used for the same purpose. The denoising AE data cleaning algorithm is also shown to be very effective in recovering clean data from corrupted (attacked) data. M. Aboelwafa, K. Seddik, and Y. Gadallah are with the Electronics and Communications Engineering Department at the American University in Cairo, New Cairo, 11835 Egypt (e-mail: mariam.aboelwafa@aucegypt.edu, kseddik@aucegypt.edu, ygadallah@ieee.org). M. Eldefrawy is with the School of Information Technology, Halmstad University, 301 18 Halmstad, Sweden (e-mail: mohamed.eldefrawy@hh.se). M. Gidlund is with the Department of Information Systems and Technology, Mid Sweden University, 851 70 Sundsvall, Sweden (e-mail: mikael.gidlund@miun.se) Parts of this work was funded by STINT under grant IB2018-7469 Copyright (c) 2020 IEEE. Personal use of this material is permitted. However, permission to use this material for any other purposes must be obtained from the IEEE by sending a request to pubs-permissions@ieee.org.

The Internet of Things (IoT) is rapidly becoming an integral component of the industrial market in areas such as automation and analytics, giving rise to what is termed as the Industrial IoT (IIoT). The IIoT promises innovative business models in various industrial domains by providing ubiquitous connectivity, efficient data analytics tools, and better decision support systems for a better market competitiveness. However, IIoT deployments are vulnerable to a variety of security threats at various levels of the connectivity and communications infrastructure. The complex nature of the IIoT infrastructure means that availability, confidentiality and integrity are difficult to guarantee, leading to a potential distrust in the network operations and concerns of loss of critical infrastructure, compromised safety of network end-users and privacy breaches on sensitive information. This work attempts to look at the requirements currently specified for a secure IIoT ecosystem in industry standards, such as Industrial Internet Consortium (IIC) and OpenFog Consortium, and to what extent current IIoT connectivity protocols and platforms hold up to the standards with regard to security and privacy. The paper also discusses possible future research directions to enhance the security, privacy and safety of the IIoT.

The deployment of the Internet of Things (IoT) in industry, called the Industrial IoT (IIoT), is supporting the introduction of very desirable improvements, such as increasing production flexibility, self-organization, and real-time and quick response to events. However, security and privacy challenges are still to be well addressed. The IIoT requires different properties to achieve secure and reliable systems and these requirements create extra challenges considering the limited processing and communication power available to IIoT field devices. In this research article, we present a key distribution protocol for IIoT that is computationally and communicationally lightweight (requires a single message exchange) and handles node addition and revocation, as well as fast rekeying. The scheme can also resist the consequences of node capture attacks (we assume that captured nodes can be detected by the gateway and previous works have shown this assumption to be acceptable in practice), server impersonation attacks and provides forward/backward secrecy. We show formally the correctness of our protocol and evaluate its energy consumption under realistic scenarios using a real embedded platform compared to previous state-of-the-art key-exchange protocols, to show our protocol reliability for IIoT.

The Internet of Things (IoT) can be defined as the network of physical objects, or "things," embedded with sensors and software for processing and exchanging data with other devices and ecosystems using the Internet as a medium. With its rapid growth over the past decade, it has permeated several application domains, including intelligent vehicular systems. The Internet of Autonomous Vehicles (IoAV) is a subset of IoT that envisions dynamic autonomous driving without human intervention. The dynamic nature of the environment in which autonomous vehicles operate introduces significant challenges, such as real-time communication and security vulnerabilities. These challenges cannot be directly addressed by standard cybersecurity solutions designed primarily for static IoT environments. In this work, we outline the various vulnerabilities of the IoAV systems, and we delve into the critical importance of adopting lightweight security protocols. These protocols are crucial to ensure robust protection while at the same time not jeopardizing the performance of the IoAV system. We also highlight the fast lightweight security protocols implemented on heterogeneous embedded, low-power, high-performance computing platforms as a viable solution to address these challenges.