Journal ArticleDOI
Network Intrusion Detection for IoT Security Based on Learning Techniques
TLDR
This survey classifies the IoT security threats and challenges for IoT networks by evaluating existing defense techniques and provides a comprehensive review of NIDSs deploying different aspects of learning techniques for IoT, unlike other top surveys targeting the traditional systems.Abstract:
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.read more
Citations
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Journal ArticleDOI
Deep learning for cyber security intrusion detection: Approaches, datasets, and comparative study
TL;DR: A survey of deep learning approaches for cyber security intrusion detection, the datasets used, and a comparative study to evaluate the efficiency of several methods are presented.
Journal ArticleDOI
Machine learning based solutions for security of Internet of Things (IoT): A survey
TL;DR: 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, and ML-based potential solutions for IoT security has been presented and future challenges are discussed.
Journal ArticleDOI
TON_IoT Telemetry Dataset: A New Generation Dataset of IoT and IIoT for Data-Driven Intrusion Detection Systems
TL;DR: A new data-driven IoT/IIoT dataset with the ground truth that incorporates a label feature indicating normal and attack classes, as well as a type feature indicating the sub-classes of attacks targeting IoT/ IIoT applications for multi-classification problems is proposed.
Journal ArticleDOI
Survey on IoT security: Challenges and solution using machine learning, artificial intelligence and blockchain technology
TL;DR: This survey systematically study the three primary technology Machine learning(ML), Artificial intelligence (AI), and Blockchain for addressing the security issue in IoT.
Journal ArticleDOI
Passban IDS: An Intelligent Anomaly-Based Intrusion Detection System for IoT Edge Devices
TL;DR: Passban is presented, an intelligent intrusion detection system (IDS) able to protect the IoT devices that are directly connected to it that can be deployed directly on very cheap IoT gateways, taking full advantage of the edge computing paradigm to detect cyber threats as close as possible to the corresponding data sources.
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