Machine Learning Based Solutions for Security of Internet of Things (IoT): A Survey (2004.05289v1)

Abstract: Over the last decade, IoT platforms have been developed into a global giant that grabs every aspect of our daily lives by advancing human life with its unaccountable smart services. Because of easy accessibility and fast-growing demand for smart devices and network, IoT is now facing more security challenges than ever before. There are existing security measures that can be applied to protect IoT. However, traditional techniques are not as efficient with the advancement booms as well as different attack types and their severeness. Thus, a strong-dynamically enhanced and up to date security system is required for next-generation IoT system. A huge technological advancement has been noticed in Machine Learning (ML) which has opened many possible research windows to address ongoing and future challenges in IoT. In order to detect attacks and identify abnormal behaviors of smart devices and networks, ML is being utilized as a powerful technology to fulfill this purpose. In this survey paper, the architecture of IoT is discussed, following a comprehensive literature review on ML approaches the importance of security of IoT in terms of different types of possible attacks. Moreover, ML-based potential solutions for IoT security has been presented and future challenges are discussed.

Survey of Machine Learning Approaches for IoT Security

The paper "Machine Learning Based Solutions for Security of Internet of Things (IoT): A Survey," authored by Syeda Manjia Tahsien, Hadis Karimipour, and Petros Spachos, emphasizes the application of ML techniques to enhance the security of IoT systems. As IoT devices proliferate, becoming integral to daily operations across various domains, ensuring their security is paramount. Traditional security mechanisms are often inadequate against increasingly sophisticated threats, necessitating more adaptive and intelligent methodologies such as those offered by ML.

Overview

This survey provides a comprehensive examination of the security challenges IoT architectures face and explicates how ML techniques can be leveraged to address these challenges. The architecture of IoT is primarily divided into three layers: the perception layer, the network layer, and the application layer. Each of these layers is susceptible to distinct types of security threats, necessitating tailored ML-based solutions. The paper methodically categorizes various IoT attacks, ranging from active threats like Denial of Service (DoS) attacks to passive threats such as eavesdropping, and identifies ML methods to detect and mitigate these threats.

Machine Learning Techniques and IoT Security Solutions

The survey discusses the potential of ML algorithms classified into supervised learning, unsupervised learning, and reinforcement learning (RL) as tools for enhancing IoT security. Supervised learning methods such as Support Vector Machine (SVM), Decision Tree (DT), and Random Forest (RF) are highlighted for their capabilities in intrusion detection and anomaly classification within IoT networks.

• Supervised Learning: These methods are utilized extensively in intrusion detection. The paper provides evidence of high detection accuracy with SVM and RF algorithms, particularly for DDoS detection. The convergence of classification algorithms with complex IoT data structures stands out as a promising avenue for research.
• Unsupervised Learning: Algorithms like K-means Clustering and Principal Component Analysis (PCA) reduce dimensionality, which is crucial for processing the vast volumes of data typical in IoT environments. They help in identifying novel attack vectors and anomalies without relying on pre-labeled data.
• Reinforcement Learning (RL): RL algorithms, exemplified by Q-learning, offer adaptive solutions that evolve with the changing threat landscape, demonstrating effectiveness in countering real-time attacks such as jamming and spoofing.

Challenges and Future Directions

The paper identifies significant challenges in applying ML to IoT security, such as data scarcity, computational constraints, and privacy concerns. The IoT landscape is highly dynamic, with devices generating vast volumes of largely unstructured and unlabeled data that challenge traditional ML training models. Additionally, the limited computational resources typical of many IoT devices necessitate the development of lightweight, efficient ML algorithms. Privacy leakage, due to the extensive data sharing inherent in IoT, also poses unresolved issues that could benefit from further exploration of privacy-preserving ML techniques.

Looking ahead, the survey suggests a focused exploration into automated real-time update strategies for ML models deployed in IoT settings, addressing the challenge of evolving threats. Furthermore, the paper emphasizes the need for enhanced cryptographic techniques to protect ML algorithms from adversarial attacks, ensuring robust and reliable IoT security frameworks.

Conclusion

This survey synthesizes existing research and provides a thorough framework that outlines the application of ML to alleviate the security burdens in IoT networks. By systematically addressing the interplay of IoT architecture and ML-assisted security strategies, the paper lays a groundwork for future innovations in this dynamic field. As the IoT landscape expands, ongoing research informed by this survey will be crucial in developing more effective, resilient security practices for the next generation of IoT systems.