A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security

By Mohammed Ali Al-Garadi, Amr M. Mohamed, A. Al-Ali, Xiaojiang Du, Ihsan Ali, Mohsen Guizani. IEEE Communications Surveys and Tutorials, 2018

1,073 citations recorded by the source corpus.

The Internet of Things (IoT) integrates billions of smart devices that can communicate with one another with minimal human intervention. IoT is one of the fastest developing fields in the history of computing, with an estimated 50 billion devices by the end of 2020. However, the crosscutting nature of IoT systems and the multidisciplinary components involved in the deployment of such systems have introduced new security challenges. Implementing security measures, such as encryption, authentication, access control, network and application security for IoT devices and their inherent vulnerabilities is ineffective. Therefore, existing security methods should be enhanced to effectively secure the IoT ecosystem. Machine learning and deep learning (ML/DL) have advanced considerably over the last few years, and machine intelligence has transitioned from laboratory novelty to practical machinery in several important applications. Consequently, ML/DL methods are important in transforming the security of IoT systems from merely facilitating secure communication between devices to security-based intelligence systems. The goal of this work is to provide a comprehensive survey of ML methods and recent advances in DL methods that can be used to develop enhanced security methods for IoT systems. IoT security threats that are related to inherent or newly introduced threats are presented, and various potential IoT system attack surfaces and the possible threats related to each surface are discussed. We then thoroughly review ML/DL methods for IoT security and present the opportunities, advantages and shortcomings of each method. We discuss the opportunities and challenges involved in applying ML/DL to IoT security. These opportunities and challenges can serve as potential future research directions.

Subfields: Computer Networks and Communications, Signal Processing

View Paper: https://www.semanticscholar.org/paper/da8a949f9c9f1df3a38f12c2cac97b789c705465

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Citations in the corpus

Internet of Things (IoT) Security Intelligence: A Comprehensive Overview, Machine Learning Solutions and Research Directions (2022, background): “In [25], a survey of computer and deep learning techniques for IoT security was presented.”
IoT in Smart Cities: A Survey of Technologies, Practices and Challenges (2021, methodology): “Artiﬁcial Intelligence has found applications in this area as noted by [237], for, e.g., it has been used to identify abnormal data in smart grids [238] as well as detecting attacks [239,240].”
Machine Learning in IoT Security: Current Solutions and Future Challenges (2019, background): “One of the most relevant papers to our survey is [33].”
A Review of Intrusion Detection Systems Using Machine and Deep Learning in Internet of Things: Challenges, Solutions and Future Directions (2020, background): “IoT systems face many threat dimensions from multiple directions, including user interface, cloud services, other interconnected IoT systems associated to sensors and network services [12], as shown in Figure 7.”
A Survey of Healthcare Internet-of-Things (HIoT): A Clinical Perspective (2019, background): “Other survey papers include studies focusing on communication [15], security, and data privacy aspects of the HIoT systems [16], [17].”
A Comprehensive Survey on Machine Learning-Based Big Data Analytics for IoT-Enabled Smart Healthcare System (2021, methodology): “There are various standards used by WSBN for intra communication at this layer, such as Bluetooth, ZigBee, RFID, NFC and UWB [38 – 40].”
Federated Learning in Smart City Sensing: Challenges and Opportunities (2020, neutral): “IoT is the network of devices that enables connectivity between people, things or services [8–11].”
Internet of Things Applications, Security Challenges, Attacks, Intrusion Detection, and Future Visions: A Systematic Review (2021, background): “Technological advances such as machine learning have resulted in the evolution of IoT technology [1].”
Anomaly-based intrusion detection system for IoT networks through deep learning model (2022, positive): “Moreover, deep learning techniques can intelligently predict new attacks, unlike previous threats [21].”
Unsupervised Machine Learning for Networking: Techniques, Applications and Research Challenges (2017, neutral): “[18] Arxiv 2018 282 ML applications in the internet of things ≈ ≈ √ √”
A Survey of Deep Learning Methods for Cyber Security (2019, methodology): “Additionally, [10] reviews machine learning and DL methods for securing Internet of Things (IoT) technology.”
A Survey on Blockchain for Big Data: Approaches, Opportunities, and Future Directions (2020, background): “The concept of mobile big data was reviewed in [29] and recently found many applications for next-generation wireless systems (e.g., 5G, beyond 5G, and 6G), from the physical and MAC layers to the application layer [30, 31].”
The Future of Healthcare Internet of Things: A Survey of Emerging Technologies (2020, methodology): “The application of ML in mitigating the security threats in H-IoT is demonstrated by [54].”