Analysis of machine learning-based approaches for securing the Internet of Things in the smart industry: a multivocal state of knowledge review

This study introduces the implementation of a Multivocal Literature Review (MLR) approach to analyze machine learning (ML) and deep learning (DL) strategies for securing the Industrial Internet of Things (IIoT) in smart industry environments. By extending and validating existing research and practical insights, this review aims to provide valuable guidance for both novice and intermediate audiences. The review process identified an initial pool of 403 sources (367 white literature, WL, and 36 gray literature, GL), which was refined to a set of 263 sources (247 WL and 16 GL), addressing research questions. Key contributions include: (1) a detailed classification of core technologies influencing smart industry, enhancing the identification of specific security vulnerabilities; (2) a systematic categorization of critical security challenges, including network threats, software threats, tampering and deception attacks, and advanced attacks, with a graphical summary of prevalent threats; (3) an overview of recent advancements in securing IIoT environments, encompassing theoretical frameworks, intrusion detection systems (IDS), intelligent algorithms, and datasets; and (4) a discussion on current strategy limitations, identifying open research and practice challenges. The innovative use of a MLR, incorporating diverse perspectives beyond traditional scientific literature, broadens the study's scope and improves data traceability, offering actionable recommendations for advancing ML and DL strategies in cybersecurity.