Building Low-Interactivity Multifactor Authenticated Key Exchange for Industrial Internet of Things

Complex-valued hyperchaos-assisted vector-valued artificial neural key coordination for improving security in the Industrial Internet of Things

Vehicle authentication is an essential component validating the vehicle’s identity and ensuring the integrity of transformed data for intelligent transport vehicles (ITS) in the vehicular ad hoc network (VANET). Easy to deploy and operate privacy-enhancing vehicle authentication mechanisms are the mainstay for the widespread ITS in the VANET. Very recently, VANET security architectures are constituting by IEEE 1609.2 group, NoW project, the SeVeCom project. However, these approaches heavily depend on the consuming public key infrastructure (PKI) and certification authorities (CA). In this work, walking along the research line, we attempt to design authentication protocols with two diverse factors for Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) networks, respectively, without depending on the stumbling block PKI/CA. In addition, a smooth projective hash function (SPHF) (a.k.a., a special case of the designated-verifier zero-knowledge proof system) guarantees any recipient can confirm the authenticity and integrity of the received messages without knowing the authentication factors. Thus, to optimize the communication round, SPHF is used to design a (group) two-factor authenticated key exchange (AKE) with low-interactive communication rounds. The proof-of-concept implementation indicates that the computation and communication overheads introduced by our solution are acceptable in real-world deployments. The security of the proposed approach is validated using Bellare-Pointcheval-Rogaway (BPR) model along with the experimental evaluation and the theoretical analysis.

Multi-factor based session secret key agreement for the Industrial Internet of Things

The manufacturing industry is benefiting a lot since the Industrial Internet of Things (IIoT) deploys sensors and actuators to calibrate, audit, and analyze data to enhance efficiency and profitability, thereby automating systems in the industrial sector. The exponential growth of smart devices in the industrial sector requires real‐time connectivity, low latency, nearby storage, secure communication, and reliability. To meet this demand, fog computing is effectively used in IIoT applications. Fog computing solves most of the problems in the fourth industrial revolution, which is the interface between end‐user devices and cloud/web services. Fog also allows for local processing with minimal acceptable latency. This article focuses on how fog computing becomes part of IIoT and also explores the architecture of fog‐assisted IIoT applications. The taxonomy is built by classifying the existing literature to reveal the salient features of fog computing in IIoT. This article also discusses IIoT security solutions as well as research problems of fog‐assisted IIoT. This article also outlines the research challenges of fog‐assisted IIoT and also discusses the security solutions of IIoT.

Artificial intelligence (AI) revolutionizes the productivity model and efficiency of the Industrial Internet of Things (IIoT). As a derivative of the AI era, AI-enabled IIoT drives frequent data access and intelligent industrial productivity. However, the rise of intelligence brings more sophisticated and hard-to-defend attacks against IIoT systems, such as deep identity forgery and malicious access, posing a major threat to intelligent development. Password-based Authenticated Key Agreement (AKA) is an effective cryptographic method for access security in IIoT, but current AKA schemes cannot address balancing between security, functionality and efficiency in the smart setting. To fill this gap, we propose a new password-based AKA scheme, where oblivious pseudorandom function, hash function and encryption are utilized to realize anonymous identity authentication. Considering malicious data access, we design a new token-tag mechanism with identity information to realize malicious identity tracing. In addition, our scheme supports a fast login function, helping the authorized party access data without repeating key agreements. Furthermore, formal security proofs and heuristic analyses demonstrate that our scheme is secure under multiple attacks. Finally, we compare the proposed scheme with the related schemes, and the results show that our scheme achieves the balance between safety, function and efficiency.

Nowadays, Smooth Projective Hash Functions (SPHFs) play an important role in constructing cryptographic tools such as secure Password-based Authenticated Key Exchange (PAKE) protocol in the standard model, oblivious transfer, and zero-knowledge proofs. Specifically, in this article, we focus on constructing PAKE protocol; that is, a kind of key exchange protocol which needs only a low entropy password to produce a cryptographically strong shared session key. In spite of relatively good progress of SPHFs in applications, it seems there has been little effort to build them upon quantum-resistant assumptions such as lattice-based cryptography and code-based cryptography to make them secure against quantum computer attacks. More precisely, there are two proposals based on lattice assumptions that utilize the SPHFs to construct PAKE secured in standard model. Considering quantum-resistant assumptions is less than straightforward and needs some relaxations. In this article, we introduce two new Approximate SPHF (ASPHFs) from error-correcting codes. Upon designing ASPHF, we can construct two efficient PAKE protocols. The security of our protocols could be proved based on the hardness of bounded decoding (BD) problem and learning with parity (LPN) problem in the standard model.

Industrial Internet of Things (IIoT) is ushering in huge development opportunities in the era of Industry 4.0. However, there are significant data security and privacy challenges during automatic and real-time data collection, monitoring for industrial applications in IIoT. Data security and privacy in IIoT applications are closely related to the reliability of users, which is determined by user authentication that have been widely used as an effective approach. However, the existing user authentication mechanisms in IIoT suffer from single factor authentication and poor adaptability with the rapid growth of the number of users and the diversity of user categories. To solve the aforementioned issues, this article proposes a novel Authentication mechanism based on Transfer Learning empowered Blockchain, coined ATLB. In ATLB, blockchains are applied to achieve the privacy preservation for industrial applications. In addition, by introducing the transfer learning based authentication mechanism, trustworthy blockchains are built such that the privacy preservation for industrial applications is further enhanced. Specifically, ATLB first employs a guiding deep deterministic policy gradient algorithm to train the user authentication model of a specific region, which is then transferred locally for foreign user authentication or cross-regionally for another region's user authentication such that the model training time is significantly reduced. Experimental results show that the proposed ATLB not only provides accurate authentications for IIoT applications but also achieves high throughput and low latency.

Recurrent neural networks-guided vector-valued synchronized key exchange for secure and privacy-preserving communication in Industrial Internet of Things

Efficient heterogeneous signcryption scheme based on Edge Computing for Industrial Internet of Things

As cyber attacks to Industrial Internet of Things (IIoT) remain a major challenge, blockchain has emerged as a promising technology for IIoT security due to its decentralization and immutability characteristics. Existing blockchain designs, however, introduce high computational complexity and latency challenges which are unsuitable for IIoT. This paper proposes Xyreum, a new high-performance and scalable blockchain for enhanced IIoT security and privacy. Xyreum uses a Time-based Zero-Knowledge Proof of Knowledge (T-ZKPK) with authenticated encryption to perform Mutual Multi-Factor Authentication (MMFA). T-ZKPK properties are also used to support Key Establishment (KE) for securing transactions. Our approach for reaching consensus, which is a blockchain group decision-making process, is based on lightweight cryptographic algorithms. We evaluate our scheme with respect to security, privacy, and performance, and the results show that, compared with existing relevant blockchain solutions, our scheme is secure, privacy-preserving, and achieves a significant decrease in computation complexity and latency performance with high scalability. Furthermore, we explain how to use our scheme to strengthen the security of the REMME protocol, a blockchain-based security protocol deployed in several application domains.

Many of the industries such as manufacturing, F&B, transportation have recently shown a growing interest in Industry Internet of things (IIoT) to gain competitive advantage. The IIoT is technology stack of combining internet of things, machines, computers and people enabling intelligent business transformational through advanced big data analytics. With the growing complexity of IIoT in a large-scale interconnectivity deployment, access control and authorization of IIoT objects such as smart devices, human or computers becomes the pressing requirements. In this paper, we propose SecIIoT, a practical authorization framework on annotated metadata for securing IIoT objects. It supports multi-dimension and large data processing with flexible and efficient authorization model to meet new security needs of IIoT. In order to demonstrate the validity of the proposed framework, a prototype is implemented with test results. The experimental results showed that the authorization model works efficiently in a large-scale deployment to meet new IIoT security requirements.

Internet of Things (IoT) technology has drawn the attention of the industry, where it has been able to, and still can, solve many industrial intractable issues. However, the emerging technology suffers from severe security shortcomings. Authentication is a cornerstone of IoT security, as it presents the measures of checking the legitimacy of communication entities. The Industrial IoT (IIoT) technology has special conditions, resulting from a lack of resources and a shortage of security skills. As far as we can tell, from the literature, IIoT user authentication has not been studied extensively. In 2017 Tai et al. presented an authenticated key agreement for IoT networks. Here we prove that Tai et al. scheme is susceptible to sever security weaknesses, such as; i. unknown key share attacks, ii. node capturing attacks, iii node information secrecy. In this research article, we offer an innovative IIoT user authentication protocol that can achieve secure remote user authentication without timestamping requiring precise synchronization, our protocol only needs Hashing and Xor-ing. We examine the efficiency of the presented protocol using Tmote Sky node over an MSP430 microcontroller using a COOJA simulator. we also show its correctness using the Scyther verification tool.

Today's industrial automation systems are undergoing a digital transformation that implies a shift towards the Internet of Things (IoT), leading to the Industrial Internet of Things (IIoT) paradigm. Existing Industrial Automated Control Systems (IACS), enriched with a potentially large number of IoT devices are expected to make systems more efficient, flexible, provide intelligence, and ultimately enable autonomous control. In general, the majority of such systems come with high level of criticality that calls for well-established methods and approaches when achieving cybersecurity, preferably prescribed by a standard. IEC 62443 is an industrial standard that provides procedures to manage risks related to cybersecurity threats in IACS. Given the new IIoT paradigm, it is likely that existing standards are not sufficiently aligned with the challenges related to developing and maintaining cybersecurity in such systems. In this paper we review the applicability of the IEC 62443 standard in IIoT contexts and discuss potential challenges the process owners might encounter. Our analysis underlines that some areas within the standard could prove difficult to reach compliance with. In particular, handling of cross zone communication and software updates require additional guidance.

The evolution of the Internet of Things (IoT) has empowered modern industries with the capability to implement large-scale IoT ecosystems, such as the Industrial Internet of Things (IIoT). The IIoT is vulnerable to a diverse range of cyberattacks that can be exploited by intruders and cause substantial reputational and financial harm to organizations. To preserve the confidentiality, integrity, and availability of IIoT networks, an anomaly-based intrusion detection system (IDS) can be used to provide secure, reliable, and efficient IIoT ecosystems. In this paper, we propose an anomaly-based IDS for IIoT networks as an effective security solution to efficiently and effectively overcome several IIoT cyberattacks. The proposed anomaly-based IDS is divided into three phases: pre-processing, feature selection, and classification. In the pre-processing phase, data cleaning and normalization are performed. In the feature selection phase, the candidates’ feature vectors are computed using two feature reduction techniques, minimum redundancy maximum relevance and neighborhood components analysis. For the final step, the modeling phase, the following classifiers are used to perform the classification: support vector machine, decision tree, k-nearest neighbors, and linear discriminant analysis. The proposed work uses a new data-driven IIoT data set called X-IIoTID. The experimental evaluation demonstrates our proposed model achieved a high accuracy rate of 99.58%, a sensitivity rate of 99.59%, a specificity rate of 99.58%, and a low false positive rate of 0.4%.

Industrial Internet of Things (IIoT) is a fusion of industrial automation systems and IoT systems. It features comprehensive sensing, interconnected transmission, intelligent processing, self-organization and self-maintenance. Its applications span intelligent transportation, smart factories, and intelligence. Many areas such as power grid and intelligent environment detection. With the widespread application of IIoT technology, the cyber security threats to industrial IoT systems are increasing day by day, and information security issues have become a major challenge in the development process. In order to protect the industrial IoT system from network attacks, this paper aims to study the industrial IoT information security protection technology, and the typical architecture of industrial Internet of things system, and analyzes the network security threats faced by industrial Internet of things system according to the different levels of the architecture, and designs the security protection strategies applied to different levels of structures based on the specific means of network attack.