Securing IOT Ecosystems With Ai-Based Threat Detection

Abstract

The rapid proliferation of Internet of Things (IoT) devices across critical infrastructure, healthcare, smart cities, 
and industrial environments has introduced an expansive and complex attack surface that traditional security 
mechanisms struggle to address effectively. IoT ecosystems present unique security challenges rooted in 
constrained computational resources, heterogeneous protocols, limited cryptographic support, and the sheer 
scale of deployed devices. This review paper presents a comprehensive meta-analysis of existing literature 
examining the intersection of IoT security vulnerabilities and Artificial Intelligence (AI)-driven solutions. Through 
systematic synthesis of over thirty peer-reviewed studies published between 2017 and 2024, this paper investigates 
the dominant threat categories including network intrusion, Distributed Denial of Service (DDoS) attacks, 
firmware exploitation, and adversarial manipulation and evaluates AI-based countermeasures such as machine 
learning-based intrusion detection, federated learning for privacy-preserving anomaly detection, deep 
reinforcement learning for adaptive access control, and natural language processing for vulnerability assessment. 
The analysis reveals that while supervised machine learning models achieve strong detection accuracy under 
controlled conditions, their performance degrades significantly in dynamic, resource-constrained IoT 
environments. Federated learning and lightweight neural architectures show considerable promise for edge-side 
deployment. This paper further identifies critical research gaps, including the absence of standardized IoT 
security benchmarks, the under exploration of explainable AI for security audit trails, and the limited real-world 
validation of AI security frameworks. The findings collectively advance the understanding of how AI can be 
strategically deployed to fortify IoT ecosystems against evolving cyber threats.