Journal: Mobile Networks and Applications
Year: 2022
Citations: 236
DOI: 10.1007/s11036-022-01937-3

Abstract

The Internet of Things (IoT) has revolutionized the way we interact with technology, connecting billions of devices and enabling smart applications across various domains. However, the proliferation of IoT devices has introduced significant security challenges that threaten the privacy and safety of users and systems. This comprehensive survey examines IoT security intelligence, focusing on machine learning-based solutions for detecting and mitigating security threats. We analyze various types of IoT security attacks, including device-level, network-level, and application-level threats. The paper discusses machine learning approaches for IoT security, including supervised, unsupervised, and reinforcement learning techniques. We also examine emerging trends such as federated learning and edge computing for distributed IoT security. The survey concludes with research directions and recommendations for developing more robust and intelligent IoT security systems.

Summary

This comprehensive survey addresses the critical security challenges facing the rapidly expanding Internet of Things ecosystem, where billions of connected devices create unprecedented attack surfaces and vulnerabilities. The research examines how the proliferation of IoT devices across smart homes, industrial systems, healthcare applications, and urban infrastructure has introduced complex security threats that traditional cybersecurity approaches struggle to address effectively. The study provides detailed analysis of various attack vectors including device-level vulnerabilities such as weak authentication and firmware flaws, network-level threats including man-in-the-middle attacks and denial of service, and application-level security issues.

The paper focuses extensively on machine learning-based solutions for IoT security intelligence, examining how artificial intelligence techniques can be applied to detect, prevent, and respond to security threats in real-time. The research analyzes supervised learning approaches for threat classification and detection, unsupervised learning methods for anomaly detection and discovering unknown attack patterns, and reinforcement learning techniques for adaptive security responses. The study demonstrates how these machine learning approaches can provide more dynamic and intelligent security solutions compared to traditional rule-based systems.

The research also explores emerging trends that are shaping the future of IoT security, including federated learning approaches that enable collaborative threat detection while preserving privacy, and edge computing solutions that bring security processing closer to IoT devices for faster response times. The survey addresses the unique challenges of securing resource-constrained IoT devices and distributed IoT networks, while examining how advanced machine learning techniques can be adapted for these environments. The comprehensive analysis provides valuable insights for researchers, practitioners, and policymakers working to develop more robust and intelligent IoT security frameworks.

Main Takeaways

• Complex Multi-Level Threats: IoT security faces diverse challenges across device, network, and application levels, requiring comprehensive approaches that address vulnerabilities at each layer of the IoT ecosystem.

• Machine Learning Security Intelligence: AI techniques including supervised, unsupervised, and reinforcement learning provide more dynamic and adaptive security solutions compared to traditional rule-based approaches for IoT threat detection and response.

• Resource-Constrained Environment Challenges: IoT devices’ limited processing power and memory require specialized lightweight security solutions and innovative approaches like edge computing for effective threat protection.

• Federated Learning Privacy Protection: Collaborative threat detection through federated learning enables devices to contribute to security intelligence while preserving privacy and keeping sensitive data local.

• Real-Time Adaptive Response: The research emphasizes the need for intelligent security systems that can detect, analyze, and respond to threats in real-time across distributed IoT networks.

• Emerging Technology Integration: Edge computing and distributed machine learning approaches are shaping the future of IoT security by bringing processing closer to devices and enabling more efficient threat detection and response.