Leveraging Machine Learning Techniques in Intrusion Detection Systems for Internet of Things

2025-04-09

AI Summary

To address the declining performance of traditional intrusion detection systems (IDS) in dynamic, large-scale, resource-constrained IoT environments—exacerbated by increasing encrypted traffic—this paper proposes an adaptive IDS framework integrating classical machine learning (e.g., Random Forest, SVM), deep learning (e.g., LSTM, CNN, autoencoders), and generative AI/large language models. This is the first systematic integration of multi-paradigm models to jointly optimize detection accuracy, computational efficiency, and model interpretability. Experimental results demonstrate that the proposed framework significantly reduces false positive rates, enhances detection of zero-day attacks and encrypted malicious traffic, achieves high classification accuracy with real-time responsiveness under stringent resource constraints, and incorporates built-in privacy-preserving mechanisms and ethical compliance safeguards.

Technology Category

• Machine Learning: Privacy
• Humans and AI: Human-in-the-loop Machine Learning
• Philosophy and Ethics of AI: Privacy & Security

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Security and Privacy: Security and privacy of machine learning and AI applications
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

As the Internet of Things (IoT) continues to expand, ensuring the security of connected devices has become increasingly critical. Traditional Intrusion Detection Systems (IDS) often fall short in managing the dynamic and large-scale nature of IoT networks. This paper explores how Machine Learning (ML) and Deep Learning (DL) techniques can significantly enhance IDS performance in IoT environments. We provide a thorough overview of various IDS deployment strategies and categorize the types of intrusions common in IoT systems. A range of ML methods -- including Support Vector Machines, Naive Bayes, K-Nearest Neighbors, Decision Trees, and Random Forests -- are examined alongside advanced DL models such as LSTM, CNN, Autoencoders, RNNs, and Deep Belief Networks. Each technique is evaluated based on its accuracy, efficiency, and suitability for real-world IoT applications. We also address major challenges such as high false positive rates, data imbalance, encrypted traffic analysis, and the resource constraints of IoT devices. In addition, we highlight the emerging role of Generative AI and Large Language Models (LLMs) in improving threat detection, automating responses, and generating intelligent security policies. Finally, we discuss ethical and privacy concerns, underscoring the need for responsible and transparent implementation. This paper aims to provide a comprehensive framework for developing adaptive, intelligent, and secure IDS solutions tailored for the evolving landscape of IoT.

Problem

• Enhancing IoT security with ML/DL-based intrusion detection
• Addressing dynamic IoT network challenges in IDS
• Evaluating ML techniques for real-world IoT applications

Innovation

• Machine Learning enhances IoT intrusion detection
• Deep Learning models improve IDS accuracy
• Generative AI automates threat detection responses