Provably secure lightweight certificateless lattice‐based authenticated key exchange scheme for IIoT

Growth of civil air traffic worldwide and new entrants such as Unmanned Aeronautical Vehicles (UAVs) pose a great challenge for the supporting Communication, Navigation and Surveillance (CNS) infrastructure. Analogue systems have to be replaced by digital means to optimize spectrum efficiency and automation is becoming much more important to be able to handle the amount of participants in the air traffic system.
\nAs safety and security are strongly intertwined in aviation, cybersecurity is one key enabler for digitalization in civil aviation. However, few deployed digital aeronautical communications system. 
\nLink requirements of low latency and low bandwidth make certificate based trust solutions costly, due to the comparatively high load of security data exchanged in a certificate based authenticated key exchange scheme. Thus, this paper proposes an alternative by introducing the challenge-response based Physical Unclonable Function (PUF) Mutual Authentication Key Exchange (PMAKE) scheme. Additionally, it is shown how a Keyless Signature Infrastructure (KSI) can be incorporated into the PMAKE scheme to secure the challenge-response pairs and anchor trust in a distributed way into the system.

Recent advancements of the Industrial Internet of Things (IIoT) have revolutionized modern urbanization and smart cities. While IIoT data contain rich events and objects of interest, processing a massive amount of IIoT data and making predictions in real-time are challenging. Recent advancements in artificial intelligence (AI) allow processing such a massive amount of IIoT data and generating insights for further decision-making processes. In this article, we propose several key aspects of AI-enabled IIoT data for smart city monitoring. First, we have combined a human-intelligence-enabled crowdsourcing application with that of an AI-enabled IIoT framework to capture events and objects from IIoT data in real time. Second, we have combined multiple AI algorithms that can run on distributed edge and cloud nodes to automatically categorize the captured events and objects and generate analytics, reports, and alerts from the IIoT data in real time. The results can be utilized in two scenarios. In the first scenario, the smart city authority can authenticate the AI-processed events and assign these events to the appropriate authority for managing the events. In the second scenario, the AI algorithms are allowed to interact with humans or IIoT for further processes. Finally, we will present the implementation details of the scenarios mentioned above and the test results. The test results show that the framework has the potential to be deployed within a smart city.

How to reduce communication complexity is a common important issue to design cryptographic protocols. This paper focuses on authenticated key exchange (AKE). Several AKE schemes have been studied, which satisfy strong security such as exposure-resilience in the standard model (StdM). However, there is a large gap on communication costs between schemes in the StdM and in the random oracle model. In this paper, we show a generic construction that is significantly compact (i.e., small communication cost) and secure in the StdM. We follow an existing generic construction from key encapsulated mechanism (KEM). Our main technique is to use a bounded chosen-ciphertext secure KEM instead of an ordinary chosen-ciphertext secure KEM. The communication cost can be reduced to half by this technique, and we achieve the most compact AKE scheme in the StdM. Moreover, our construction has instantiations under wider classes of hardness assumptions (e.g., subset-sum problems and multi-variate quadratic systems) than existing constructions. This work pioneers the first meaningful application of bounded chosen-ciphertext secure KEM.

Architectural changes are happening in the modern industries due to the adaption and the deployment of ‘Internet of Things (IoT)’ for monitoring and controlling various devices remotely from the external world. The most predominant place where the IoT technology makes the most sense is the industrial automation processes in smart industries (Industry 4.0). In this paper, a reliable ‘Next Generation Cyber Security Architecture (NCSA)’ is presented for Industrial IoT (IIoT) environment that detects and thwarts cybersecurity threats and vulnerabilities. It helps to automate the processes of exchanging real-time critical information between devices without any human intervention. It proposes an analytical framework that can be used to protect entities and network traffics involved in the IIoT wireless communication. It incorporates an automated cyber-defense authentication mechanism that detects and prevents security attacks when a network session has been established. The defense mechanism accomplishes the required level of security protection in the network by generating an identity token which is cryptographically encrypted and verified by a virtual gateway system. The proposed NCSA improves security in the IIoT environment and reduces operational management cost.

Many different sectors are beginning to deploy cyber‐physical systems (CPS) and industrial Internet of Things (IIoT) devices, including wearables, sensors, and critical infrastructure. In order to ensure that the device's data remains confidential, it must be encrypted prior to transmission. However, obtaining authenticity and integrity in IIoT data is also crucial, and signcryption has been proposed as a solution to this challenge. An additional obstacle is how to search for signcrypted cloud data using plaintext keywords. In this paper, we suggest a plaintext‐checkable identity‐based signcryption for CPS towards IIoT (IBSC‐PCE), which enables plaintext searching of signcrypted cloud‐based data. We provide both confidentiality and integrity and also demonstrate the security of our approach under the q‐Diffie‐Hellman inversion problem (q‐DHIP). This problem has been shown to be secure in the random oracle model (ROM), which further reinforces the robustness of our approach. Compared to current state‐of‐the‐art methods, our proposed approach outperforms them.

CMAF-IIoT: Chaotic map-based authentication framework for Industrial Internet of Things

The rapid integration of industrial automation and control systems into large-scale Industrial Internet of Things (IIoT) infrastructures has substantially increased system complexity. The complexity arises from the deployment of heterogeneous devices that must coordinate in real-time to support adaptive decision-making. As a result, a robust situation awareness (SA) framework for IIoT has become a critical goal of IIoT. This review investigates the role of SA in the design of controllers in industrial applications, with the aim of advancing control strategies and facilitating integration with emerging IoT technologies. The survey focuses on SA, Model Predictive Control (MPC), and their integration, based on time series as a standard data structure to represent device performance. A key finding from the current literature is that SA is often treated separately from control theory, resulting in isolated applications and not integrated with large-scale IIoT systems. This survey establishes the features and paradigms of SA in IIoT. It provides a structured analysis of controller design methods in IIoT. State-space models (SSMs) can be used to model temporal dynamics in IIoT data. Finally, the integration of artificial intelligence (AI) with SSMs is explored to achieve SA based on multivariate time series data.

The evolution of the Industrial Internet of Things (IIoTs) greatly increases the volume of data generated by the connected IIoT devices. IIoT data are playing an increasingly important role in various industrial sectors. IIoT data sharing helps enterprises make better production decisions and respond to market changes timely. However, the distrust among IIoT entities and IIoT entities' distrust of data-sharing platforms may hinder the realization of data sharing. In this article, a decentralized IIoT data-sharing scheme based on blockchain and edge computing is proposed. A Proof of Storage and Transmission (PoST) consensus mechanism is proposed to meet data storage and transmission requirements of data owners in IIoT data-sharing networks. Based on the manufacture ties of data owners, shared data request probabilities are derived. The IIoT data sharing interactions between data owners and edge devices are modeled as a multiple-leader and multiple-follower Stackelberg game. The alternating direction method of multipliers (ADMMs) algorithm is used to obtain the optimal IIoT data sharing solutions in a distributed manner. Simulation results show that compared with the cooperative scheme, the total profit of edge devices is maximally increased by 59%, and the total utility of data owners is maximally increased by 52%.

Industrial Internet of Things (IIoT) is present in many participants from the energy, health, manufacturing, transport, and public sectors. Many factors catalyze IIoT, such as robotics, artificial intelligence, and intelligent decentralized manufacturing. However, the convergence between IT, OT, and IoT environments involves the integration of heterogeneous technologies through protocols, standards, and buses. However, this integration brings with it security risks. To avoid the security risks, especially when systems in different environments interact, it is important and urgent to create an early consensus among the stakeholders on the IIoT security. The default Common Vulnerability Scoring System (CVSS) offers a mechanism to measure the severity of an asset's vulnerability and therefore a way to characterize the risk. However, CVSS by default has two drawbacks. On the one hand, to carry out a risk analysis, it is necessary to have additional metrics to the one established by CVSSv3.1. On the other hand, this index has been used mostly in IT environments and although there are numerous efforts to develop a model that suits industrial environments, there is no established proposal. Therefore, we first propose a survey of the main 33 protocols, standards, and buses used in an IIoT environment. This survey will focus on the security of each one. The second part of our study consists of the creation of a framework to characterize risk in industrial environments, i.e., to solve both problems of the CVSS index. To this end, we created the Vulnerability Analysis Framework (VAF), which is a methodology that allows the analysis of 1,363 vulnerabilities to establish a measure to describe the risk in IIoT environments.

Energy efficiency plays a crucial role in the field of industrial automation, particularly in relation to conveyor belt systems that operate continuously in various sectors, including mining, logistics, and manufacturing. Smart Industrial Components (SIC) are required to process, manage, and produce a substantial amount of different types of IIOT (Industrial Internet of Things) data. This historical data is the main source for analysing the current and future conditions of the components. In this paper, a predictive analytics algorithm is proposed to analyse the trend of real-time and future power consumption. The proposed algorithm seamlessly combines generative AI with IIoT data to enhance predictive modelling capabilities. This study highlights key challenges, methodologies, and future directions in leveraging AI-driven analytics for industrial energy management, as well as investigates the application of predictive data analytics for conveyor belt systems focusing on two key aspects: (1) historical data analysis to monitor the current condition of the conveyor belt system and (2) the use of generative AI for future prediction essential for analysing future trends of power consumption running at the specific condition.

A comparative evaluation of intrusion detection systems on the edge-IIoT-2022 dataset

Mobile robot is an important developing direction in the field of robotics, it is widely used in Industrial Internet of Things (IIoT) environment, agriculture, military, transportation, services with the coming of 5G wireless communication technology. Automatic navigation control technology is the core in these research areas, which is also the key technology for mobile robot to achieve intelligentization and autonomation.The article discusses and researches the neural network technology and its application in mobile robot navigation control. For the characteristics and research of mobile robot navigation problem, it finds the way to improve the mobile robot intelligentization, level of the self-organization, self-learning and adaptive capability. The combination of neural network with other intelligent algorithms solves autonomous navigation problem of the mobile robot in the complex uncertain environments and unknown variable environments. The mobile robot navigation control problem using fuzzy neural network can achieve a more effective real-time navigation control performance through amending the network weights by self-study according to the navigation priori knowledge of human experts.

An efficient and provable certificate-based proxy signature scheme for IIoT environment

The industrial Internet of things (IIoT) is a framework that enables companies to improve their processes and increase their efficiency by using smart sensors and actuators. It is also used to collect data from various industrial devices and make use of this data to improve their operations. With the help of connected sensors and actuators, companies can save time. Due to the increasing number of industrial Internet of things (IIoT) devices, the data generated by these networks is constantly being analyzed. However, due to the high dimensionality of the data, it can cause fragmentation. Also, datasets collected by IIoT nodes are prone to displaying anomalous events. Unlike traditional networks, which are usually composed of several applications and protocols, IIoT networks are different. They require more stringent security measures to protect the integrity and confidentiality of their data. Due to the unique features of IIoT networks, they are prone to attack. This paper briefly describes the various requirements and challenges faced when it comes to protecting these networks. It also presents evaluation of various machine learning classifiers to detect anomalous behaviors in these networks. Due to the nature of the data that these networks contain anomalies, their analysis is critical to prevent attacks. In this paper, we analyze the usage of various machine learning algorithms to check the efficiency and accuracy of detecting anomalies in data generated by crucial IIoT devices.

Industrial Internet of Things (IIoT) finds use in several industrial applications like robots, medical devices, and software-defined manufacturing processes. Apart from the promising benefits of the IIoT networks, several challenging issues need to be resolved, such as network connectivity, security, privacy, heterogeneity, scheduling, and energy efficiency. Due to the large-scale deployment and heterogeneity of the nodes in IIoT networks, some energy-limited nodes have existed in the IIoT networks, resulting in reduced network lifetime. At the same time, security and privacy are also considered as the major issues that exist in the design of IIoT, which can be addressed using secure routing techniques. In this view, this study develops a trust-aware multiobjective metaheuristic optimization-based secure clustering with route planning (TAMOMO-SCRP) technique for cluster-based IIoT environment. The presented TAMOMO-SCRP technique mainly focuses on the design of bald eagle search (BES) algorithm for clustering and routing processes. The proposed TAMOMO-SCRP model derives a fitness function for accomplishing maximum energy efficiency and security. For an effective clustering process, the TAMOMO-SCRP model designs an objective function involving four parameters such as trust level (TL), communication cost (CC), residual energy (RE), and node degree (ND). Besides, the route selection process is based on the fitness function with two variables, namely, queue length and link quality. For assessing the enhanced performance of the TAMOMO-SCRP model, a wide range of experiments were carried out to get the outcomes of network life time(NLT) as 39451,half network die (HND) as 25950 and Stability period (SP) 8000 time calculated no. of alive nodes. The achieved outcomes make sure the better performance of the TAMOMO-SCRP technique against the other recent approaches.