Foundations of IoT Security and the Evolving Threat Landscape
Download PDFAbstract
The rapid proliferation of Internet of Things (IoT) devices has fundamen tally transformed the digital ecosystem, connecting billions of physical objects to the internet. However, this unprecedented connectivity has introduced a complex and rapidly evolving threat landscape. This chapter provides a comprehensive exploration of the foundational principles of IoT security and analyzes contemporary cyber threats target ing these interconnected systems. By examining the structural vulnerabilities inherent in IoT architectures, including limited computational resources, heterogeneous protocols, and widespread deployment in unmanaged environments, we identify the primary vectors exploited by malicious actors. The chapter introduces a robust research methodology for analyzing IoT network traffic using advanced machine learning techniques, leverag ing the CIC IoT Dataset to demonstrate practical intrusion detection capabilities. Our experimental results reveal the efficacy of ensemble learning methods in identifying anoma lous behavior and mitigating sophisticated attacks such as Distributed Denial of Service (DDoS) and unauthorized access attempts. Ultimately, this chapter serves as a founda tional guide for understanding the current state of IoT security, offering strategic insights and scalable methodologies to fortify the next generation of connected devices against emerging cyber threats.
References
- Veikka R¨onkk¨o. “Challenges and Opportunities for Network Access Control System Implemented into Enterprise Network (s)”. In: (2025).
- Brian D Huyghue. “Cybersecurity, internet of things, and risk management for busi nesses”. MA thesis. Utica College, 2021.
- J Malakai Bailey et al. “Convergence of Operational Technology/Industrial Control Systems/Internet of Medical Things: Internet-Exposed Medical Device Threats”. In: 2025 IEEE 7th International Conference on Trust, Privacy and Security in In telligent Systems, and Applications (TPS-ISA). IEEE. 2025, pp. 517–525.
- Sydney Mambwe Kasongo and Yanxia Sun. “A deep learning method with wrapper based feature extraction for wireless intrusion detection system”. In: Computers & Security 92 (2020), p. 101752.
- Mehdi Houichi, Faouzi Jaidi, and Adel Bouhoula. “Enhancing Smart City Security: An Intrusion Detection System Using Machine Learning Methods With the UNB CIC IoT 2023 Dataset”. In: IET Smart Cities 7.1 (2025), e70014.
- Massimo Nardone. “Foundations of IoT and the Security Landscape Description”. In: Securing Smart Things: A Hands-On Guide to Safeguarding Smart Systems. Springer, 2026, pp. 1–26.
- Akashdeep Bhardwaj. “Evolving Threat Landscape in IoT and IIoT Environments”. In: Smart and Agile Cybersecurity for IoT and IIoT Environments. IGI Global Sci entific Publishing, 2024, pp. 27–49.
SECURE AND SCALABLE INTERNET OF THINGS
