Trends in IoT intrusion detection: A bibliometric analysis of deep learning approaches - Amir Muhammad Hafiz, Othman; Mohd Faizal, Ab Razak; Ahmad Firdaus, Zainal Abidin; Syazwani, Ramli; Wan Nur Syamilah, Wan Ali (Insight Society, 2025)
The Internet of Things (IoT) has transformed modern technology by interconnecting devices and systems, improving efficiency and functionality across various domains. However, its rapid expansion has also introduced significant security vulnerabilities, necessitating the development of robust intrusion detection systems (IDS) to counter evolving cyber threats. Despite advancements in IDS research, particularly through deep learning integration, a systematic bibliometric analysis assessing global research trends, key contributors, and collaboration networks remains lacking. This study addresses that gap by conducting a bibliometric analysis of IDS for IoT using deep learning, focusing on articles published between 2016 and 2024 in the Scopus database. It examines global research trends, keyword co-occurrences, publication patterns, citation dynamics, and international collaborations, offering a comprehensive overview of the field. The findings indicate a significant rise in IDS research, with India, China, the United States, and Saudi Arabia emerging as leading contributors and collaborators. The analysis also highlights influential authors and institutions driving advancements in deep learning for IoT security. Keyword analysis reveals the prominence of terms such as "machine learning," "deep learning," and "intrusion detection," underscoring the field’s focus on artificial intelligence for IoT security. This bibliometric study enhances the understanding of research dynamics in IDS for IoT, identifies gaps for future exploration, and provides valuable insights to drive innovation and global collaboration in this critical area of cybersecurity.