The Industrial Internet of Things (IIoT) is a physical information system developed based on traditional industrial control networks. As one of the most critical infrastructure systems, IIoT is also a preferred target for adversaries engaged in advanced persistent threats (APTs). To address this issue, we explore a deep-learning-based proactive APT detection scheme in IIoT. In this scheme, considering the characteristics of long attack sequences and long-term continuous APT attacks, our solution adopts a well-known deep learning model, bidirectional encoder representations from transformers (BERT), to detect APT attack sequences. The APT attack sequence is also optimized to ensure the model's long-term sequence judgment effectiveness. The experimental results not only show that the proposed deep learning method has feasibility and effectiveness for APT detection, but also certify that the BERT model has better accuracy and a lower false alarm rate when detecting APT attack sequences than other time series models.

With the new industrial revolution of digital transformation, more intelligence and autonomous systems can be adopted in the manufacturing transportation processes. Safety and security of autonomous vehicles (AVs) have obvious advantages of reducing accidents and maintaining a cautious environment for drivers and pedestrians. Therefore, the transformation to data-driven vehicles is associated with the concept of digital twin, especially within the context of AV design. This also raises the need to adopt new safety designs to increase the resiliency and security of the whole AV system. To enable secure autonomous systems for smart manufacturing transportation in an end-to-end fashion, this article presents the main challenges and solutions considering safety and security functions. This article aims to identify a standard framework for vehicular digital twins that facilitate the data collection, data processing, and analytics phases. To demonstrate the effectiveness of the proposed approach, a case study for a vehicle follower model is analyzed when radar sensor measurements are manipulated in an attempt to cause a collision. Perceptive findings of this article can pave the way for future research aspects related to employing digital twins in the AV industry.

Fifth-generation (5G) network developers need to identify the necessary requirements toward additional capacity and spectrally efficient wireless technologies. Therefore, the significant amount of underutilized spectrum in the Wi-Fi band is motivating operators to combine long-term evolution (LTE) with Wi-Fi technologies. This new LTE in unlicensed band (LTE-U) has the physical layer topology to access Wi-Fi spectrum, specifically the 5-GHz band. Nevertheless, the evolution of LTE-U affects the Wi-Fi operations due to the absence of any regularity for LTE-U transmissions in unlicensed band. In this article, we address the challenges for Wi-Fi to maintain transmissions under the umbrella of LTE-U as Wi-Fi is pushed offline because of the listen-before-talk (LBT) feature. Therefore, we derive a new adaptive LBT mechanism and and virtualized core network for the best practices in both Wi-Fi and LTE-U technologies. The proposed solutions include noncoordinated and coordinated network managements to enable coexistence between both technologies using tradeoff performance for fair spectrum sharing. We concentrate on the initial coexistent technique and discuss how it maps to higher-layer improvements. This article shows new approaches to achieve the Third-Generation Partnership Project (3GPP) Release 13.

Future cellular systems will employ the so-called big three technologies: UDN, massive MIMO, and mmWave (and/or terahertz) communications. The goal is to support the explosive demands for mobile broadband services foreseen for the next decade. In this work, we investigate the joint impact of the three technologies. Using system-level simulations, we evaluate the performance of two-tier cellular networks with μ Wave macrocells and densely deployed mmWave small cells. Our results show that much higher capacity can be realized with UDNs than in macrocell-only setups. The results also reveal that performance does not scale proportionally with increase in the employed mmWave bandwidth. The corresponding increase in noise (due to larger bandwidths) reduces the SINR. Outdoor users experience promising data rates notwithstanding, but the throughputs of indoor users are highly degraded. This is due to the additional wall and indoor losses (on top of the inherently high path loss at mmWave frequencies), which further reduce the SINR of indoor users. Therefore, techniques that significantly enhance the SINR are highly import ant design considerations in unleashing the amazing prospects and potentials of mmWave UDNs.

The growth of the Internet of Things (IoT) offers numerous opportunities for developing industrial applications such as smart grids, smart cities, smart manufacturers, etc. By utilising these opportunities, businesses engage in creating the Industrial Internet of Things (IIoT). IoT is vulnerable to hacks and, therefore, requires various techniques to achieve the level of security required. Furthermore, the wider implementation of IIoT causes an even greater security risk than its benefits. To provide a roadmap for researchers, this survey discusses the integrity of industrial IoT systems and highlights the existing security approaches for the most significant industrial applications. This paper mainly classifies the attacks and possible security solutions regarding IoT layers architecture. Consequently, each attack is connected to one or more layers of the architecture accompanied by a literature analysis on the various IoT security countermeasures. It further provides a critical analysis of the existing IoT/IIoT solutions based on different security mechanisms, including communications protocols, networking, cryptography and intrusion detection systems. Additionally, there is a discussion of the emerging tools and simulations used for testing and evaluating security mechanisms in IoT applications. Last, this survey outlines several other relevant research issues and challenges for IoT/IIoT security.

Software defined networking (SDN) is a key enabling technology of industrial Internet of things (IIoT) that provides dynamic reconfiguration to improve data network robustness. In the context of smart grid infrastructure, the strong demand of seamless data transmission during critical events (e.g. failures or natural disturbances) seems to be fundamentally shifting energy attitude towards emerging technology. Therefore, SDN will play a vital role on energy revolution to enable flexible interfacing between smart utility domains and facilitate the integration of mix renewable energy resources to deliver efficient power of sustainable grid. In this regard, we propose a new SDN platform based on IIoT technology to support resiliency by reacting immediately whenever a failure occurs to recover smart grid networks using real-time monitoring techniques. We employ SDN controller to achieve multi-functionality control and optimization challenge by providing operators with real-time data monitoring to manage demand, resources and increasing system reliability. Data processing will be used to manage resources at local network level by employing SDN switch segment, which is connected to SDN controller though IIoT aggregation node. Furthermore, we address different scenarios to control packet flows between switches on hub-to-hub basis using traffic indicators of the infrastructure layer, in addition to any other data from the application layer. Extensive experimental simulation is conducted to demonstrate the validation of the proposed platform model. The experimental results prove the innovative SDN based IIoT solutions can improve grid reliability for enhancing smart grid resilience.