Smart manufacturing is pivotal in the context of Industry 4.0, as it integrates advanced technologies like the Internet of Things (IoT) and automation to streamline production processes and improve product quality, paving the way for a competitive industrial landscape. Machines have become network-based through the IoT, where integrated and collaborated manufacturing system responds in real time to meet demand fluctuations for personalized customization. Within the network-based manufacturing system (NBMS), mobile industrial robots (MiRs) are vital in increasing operational efficiency, adaptability, and productivity. However, with the advent of IoT-enabled manufacturing systems, security has become a serious challenge because of the communication of various devices acting as mobile nodes. This paper proposes the framework for a newly personalized customization factory, considering all the advanced technologies and tools used throughout the production process. To encounter the security concern, an IoT-enabled NBMS is selected as the system model to tackle a black hole attack (BHA) using the NTRUEncrypt cryptography and the ad hoc on-demand distance-vector (AODV) routing protocol. NTRUEncrypt performs encryption and decryption while sending and receiving messages. The proposed technique is simulated by network simulator NS-2.35, and its performance is evaluated for different network environments, such as a healthy network, a malicious network, and an NTRUEncrypt-secured network based on different evaluation metrics, including throughput, goodput, end-to-end delay, and packet delivery ratio. The results show that the proposed scheme performs safely in the presence of a malicious node. The implications of this study are beneficial for manufacturing industries looking to embrace IoT-enabled subtractive and additive manufacturing facilitated by mobile industrial robots. Implementation of the proposed scheme ensures operational efficiency, enables personalized customization, and protects confidential data and communication in the manufacturing ecosystem.

Smart manufacturing is transforming the manufacturing industry by enhancing productivity and quality, driving growth in the global economy. The Internet of Things (IoT) has played a crucial role in realizing Industry 4.0, where machines can communicate and interact in real-time. Despite these advancements, security remains a major challenge in developing and deploying smart manufacturing. As cyber-attacks become more prevalent, researchers are making security a top priority. Although IoT and Industrial IoT (IIoT) are used to establish smart industries, these systems remain vulnerable to various types of attacks. To address these security issues, numerous authentication methods have been proposed. However, many of these methods are vulnerable to known attacks, such as physical security, privileged-insider, and impersonation attacks, or have high computational and communication costs, making them unsuitable for resource-limited IoT devices. Therefore, in this paper, we present a new approach to mutual authentication between the flexible manufacturing system unit, the user, and the server. It enables secure communication in an IIoT-enabled system, which represents the smart manufacturing industry. This security is achieved through the establishment of session keys. The security analysis shows the effectiveness of the proposed solution in resisting various types of attacks. Additionally, a comparison with other existing schemes reveals that our proposal outperforms them in terms of performance.

Smart manufacturing has become a vital technique for increasing productivity and efficiency. Firms are following a smart manufacturing implementation system to compete in the market. Therefore, it is mandatory to find the crucial factors that enable the implementation of intelligent manufacturing in enterprises. This study proposes the framework for a new model factory based on the three-dimensional model that extends the product lifecycle layer. It also analyzes the significant attributes and interdependence relationships of causes and effects through the fuzzy DEMATEL approach for the selected small and medium enterprises discussed as a case study. The results show that the factors in Region 1 are significant attributes that need to be focused on for the development and establishment of small and medium enterprises under consideration. These attributes include design documentation (A11), intelligently management of small and medium enterprises (A3), visualization and monitoring of logistics and production (A6), flow of information, energy, and materials (A12), management platform and data acquisition for equipment (A7), and visualization of quality and process (A5). The sensitivity analysis is also performed to check the results’ validity, reliability, and robustness. This study aids any manufacturing firm in analyzing the critical attributes that contribute to implementing smart manufacturing.

The ever-evolving Internet of Things (IoT) has ushered in a new era of intelligent manufacturing across multiple industries. However, the security and privacy of real-time data transmitted over the public channel of the industrial IoT (IIoT) remain formidable challenges. Existing lightweight protocols often omit one or more critical security features, such as anonymity and untraceability, and are susceptible to threats like desynchronization attacks. Additionally, they struggle to achieve an optimal balance between robust security and performance efficiency. To bridge these gaps, we introduce a new lightweight key agreement security scheme that guarantees secure access to the IIoT-enabled flexible manufacturing system (FMS). The strength of our scheme lies in its utilization of the authenticated encryption with associative data (AEAD) primitive, AEGIS, along with hash functions and physical unclonable functions, which secure the IIoT ecosystem. Additionally, our scheme offers flexibility in the form of the addition of new machines, password updates, and revocation in cases of theft or loss. A comprehensive security analysis demonstrates the efficacy of the proposed scheme in thwarting various attacks. The formal analysis, based on the Real-Or-Random (RoR) model, ensures session key indistinguishability, while the informal analysis highlights its resilience against known attacks. The comparative assessment demonstrate that the proposed scheme consistently outperforms the benchmark schemes across multiple dimensions, including security and functionality features, computational and communication overheads, and runtime efficiency. Specifically, the proposed scheme achieves peak performance enhancements of 77.55%, 44.73%, and 69.6% in computational overhead, runtime overhead, and communication overhead, respectively, underscoring its substantial performance advantages.

In the realm of contemporary manufacturing, the imperative of cybersecurity cannot be overstated. As industries globally embrace digital transformation, the fabric of manufacturing has undergone a profound metamorphosis, pivoting towards an interconnected and data-driven paradigm. This evolution, while driving efficiency and innovation, concurrently amplifies the risk landscape, rendering cybersecurity an indispensable pillar of modern manufacturing strategies. The advent of Industry 4.0 marks a seminal shift in manufacturing processes. The advanced technologies are not mere incremental enhancements; they represent a radical rethinking of manufacturing operations, offering unprecedented levels of productivity and flexibility. However, this technological renaissance is a double-edged sword. The integration of Internet of Things (IoT) devices and artificial intelligence (AI) into manufacturing systems introduces a complex web of cybersecurity challenges. IoT devices, often designed with convenience and functionality in mind rather than security, can become unwitting gateways for cyberattacks. AI algorithms, while optimizing processes, can also be exploited to disrupt operations or manipulate output. The expansive connectivity inherent in Industry 4.0 architectures further complicates the scenario, as each connected device or system potentially increases the attack surface available to malicious entities. <!----> <!----> <!----> <!---->