Network Intrusion Detection for IoT Security Based on Learning Techniques

By Nadia Chaabouni, Mohamed Mosbah, Akka Zemmari, Cyrille Sauvignac, Parvez Faruki. IEEE Communications Surveys and Tutorials, 2019

766 citations recorded by the source corpus.

Pervasive growth of Internet of Things (IoT) is visible across the globe. The 2016 Dyn cyberattack exposed the critical fault-lines among smart networks. Security of IoT has become a critical concern. The danger exposed by infested Internet-connected Things not only affects the security of IoT but also threatens the complete Internet eco-system which can possibly exploit the vulnerable Things (smart devices) deployed as botnets. Mirai malware compromised the video surveillance devices and paralyzed Internet via distributed denial of service attacks. In the recent past, security attack vectors have evolved bothways, in terms of complexity and diversity. Hence, to identify and prevent or detect novel attacks, it is important to analyze techniques in IoT context. This survey classifies the IoT security threats and challenges for IoT networks by evaluating existing defense techniques. Our main focus is on network intrusion detection systems (NIDSs); hence, this paper reviews existing NIDS implementation tools and datasets as well as free and open-source network sniffing software. Then, it surveys, analyzes, and compares state-of-the-art NIDS proposals in the IoT context in terms of architecture, detection methodologies, validation strategies, treated threats, and algorithm deployments. The review deals with both traditional and machine learning (ML) NIDS techniques and discusses future directions. In this survey, our focus is on IoT NIDS deployed via ML since learning algorithms have a good success rate in security and privacy. The survey provides a comprehensive review of NIDSs deploying different aspects of learning techniques for IoT, unlike other top surveys targeting the traditional systems. We believe that, this paper will be useful for academia and industry research, first, to identify IoT threats and challenges, second, to implement their own NIDS and finally to propose new smart techniques in IoT context considering IoT limitations. Moreover, the survey will enable security individuals differentiate IoT NIDS from traditional ones.

Subfields: Artificial Intelligence, Computer Networks and Communications, Signal Processing

View Paper: https://www.semanticscholar.org/paper/f4ba147e57beee5e57cc4548feb130cbe16c9192

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Citations in the corpus

TON_IoT Telemetry Dataset: A New Generation Dataset of IoT and IIoT for Data-Driven Intrusion Detection Systems (2020, background): “Man-In-The-Middle (MITM) attack [9] is a well-known network attack that can intercept the communication channel between two devices and may manipulate their data.”
Deep learning for cyber security intrusion detection: Approaches, datasets, and comparative study (2020, background): “[17] 2019 Partial Yes No Partial Partial Berman et al.”
Edge Learning for B5G Networks With Distributed Signal Processing: Semantic Communication, Edge Computing, and Wireless Sensing (2022, neutral): “In particular, the study [132] analyzed the threats and challenges faced by Internet-of-Things (IoT), including imperson-”
Security and Privacy for 6G: A Survey on Prospective Technologies and Challenges (2021, background): “Endpoint/network nodes [178], [179] DDos attacks Adversarial attacks Traffic meta profile Firewall/IDS/MTD AI-empowered Firewall/IDS/MTD I Breakthroughs in AI I High computing Adversarial defense”
Adversarial Machine Learning for Network Intrusion Detection Systems: A Comprehensive Survey (2023, methodology): “In particular, these surveys provide a taxonomy of NIDS with an explanation of typical ML and DL algorithms used and the characteristics of NIDS input space [22], [23], [24], [25].”
Internet of Things (IoT) Security Intelligence: A Comprehensive Overview, Machine Learning Solutions and Research Directions (2022, methodology): “In [38], the authors use several such NSL-KDD”
A Review of Intrusion Detection Systems Using Machine and Deep Learning in Internet of Things: Challenges, Solutions and Future Directions (2020, background): “In a recent survey paper published in [37], learning-based NIDSs for IoT systems were discussed in an overview of ML-based NIDSs for IoT systems.”
A critical review of intrusion detection systems in the internet of things: techniques, deployment strategy, validation strategy, attacks, public datasets and challenges (2021, methodology): “The updated survey of the taxonomy of IoT IDS discipline is presented in this paper further enhances taxonomies given in (Khraisat et al., 2019a; Benkhelifa et al., 2018; Chaabouni et al., 2019; Liao et al., 2013a).”
Passban IDS: An Intelligent Anomaly-Based Intrusion Detection System for IoT Edge Devices (2020, result): “In another similar work, Chaabouni et al. [42] discussed IoT security threats and challenges in detail.”