Intrusion detection systems for IoT-based smart environments: a survey

By M. F. Elrawy, Ali Ismail Awad, Hesham F. A. Hamed. Journal of Cloud Computing, 2018

392 citations recorded by the source corpus.

One of the goals of smart environments is to improve the quality of human life in terms of comfort and efficiency. The Internet of Things (IoT) paradigm has recently evolved into a technology for building smart environments. Security and privacy are considered key issues in any real-world smart environment based on the IoT model. The security vulnerabilities in IoT-based systems create security threats that affect smart environment applications. Thus, there is a crucial need for intrusion detection systems (IDSs) designed for IoT environments to mitigate IoT-related security attacks that exploit some of these security vulnerabilities. Due to the limited computing and storage capabilities of IoT devices and the specific protocols used, conventional IDSs may not be an option for IoT environments. This article presents a comprehensive survey of the latest IDSs designed for the IoT model, with a focus on the corresponding methods, features, and mechanisms. This article also provides deep insight into the IoT architecture, emerging security vulnerabilities, and their relation to the layers of the IoT architecture. This work demonstrates that despite previous studies regarding the design and implementation of IDSs for the IoT paradigm, developing efficient, reliable and robust IDSs for IoT-based smart environments is still a crucial task. Key considerations for the development of such IDSs are introduced as a future outlook at the end of this survey.

Subfields: Computer Networks and Communications, Signal Processing

View Paper: https://www.semanticscholar.org/paper/a7eea1d6dfbcee9a434e9ab6c9a7f67f1506d3db

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Citations in the corpus

Anomaly-based intrusion detection system for IoT networks through deep learning model (2022, positive): “In this research work, we were determined to find techniques in IoT environments to learn the various scenarios and vulnerabilities of IoT applications [15].”
Internet of Things (IoT) Security Intelligence: A Comprehensive Overview, Machine Learning Solutions and Research Directions (2022, background): “[43], the global smart city services market is expected to be 225.”
Passban IDS: An Intelligent Anomaly-Based Intrusion Detection System for IoT Edge Devices (2020, neutral): “[41] presented a comprehensive survey of IDSs along with techniques for IoT systems.”
Federated Learning for Malware Detection in IoT Devices (2021, background): “However, simultaneously to the advances in new technologies, the number and variety of cyberattacks have grown in recent years, making current security approaches outdated in a short time [2].”