Security Framework for Network-Based Manufacturing Systems with Personalized Customization: An Industry 4.0 Approach

Muhammad Hammad; Rashad Maqbool Jillani; Sami Ullah; Abdallah Namoun; Ali Tufail; Ki-Hyung Kim; Habib Shah

doi:10.3390/s23177555

Security Framework for Network-Based Manufacturing Systems with Personalized Customization: An Industry 4.0 Approach

Sensors (Basel). 2023 Aug 31;23(17):7555. doi: 10.3390/s23177555.

Authors

Muhammad Hammad¹, Rashad Maqbool Jillani², Sami Ullah³, Abdallah Namoun⁴, Ali Tufail⁵, Ki-Hyung Kim⁶, Habib Shah⁷

Affiliations

¹ Faculty of Mechanical Engineering, GIK Institute of Engineering Sciences and Technology, Topi 23640, Pakistan.
² Faculty of Computer Science and Engineering, GIK Institute of Engineering Sciences and Technology, Topi 23640, Pakistan.
³ Department of Computer Science, Shaheed Benazir Bhutto University, Sheringal 18050, Pakistan.
⁴ Faculty of Computer and Information Systems, Islamic University of Madinah, Madinah 42351, Saudi Arabia.
⁵ School of Digital Science, Universiti Brunei Darussalam, Tungku Link, Gadong BE1410, Brunei.
⁶ Department of Cyber Security, Ajou University, Suwon 16499, Republic of Korea.
⁷ Department and College of Computer Science, King Khalid University, Abha 62529, Saudi Arabia.

Abstract

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.

Keywords: Industry 4.0; NTRUEncrypt cryptography; mobile industrial robots; network-based manufacturing system; newly personalized customization factory; security; smart manufacturing.

Grants and funding

This research was partially supported by the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP2021-2021-0-01835) and the research grant (No. 2021-0-00590 Decentralized High-Performance: 2021-0-00590; IITP2021-2021-0-01835). This research was also partially supported by KIAT (Korea Institute for Advancement of Technology) grant funded by the Korea Government (MOTIE) (P0008703, The Competency Development Program for Industry Specialist) and the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2021R1F1A1045861).