A Review of Intrusion Detection Systems Using Machine and Deep Learning in Internet of Things: Challenges, Solutions and Future Directions

Asharf, Javed; Moustafa, Nour; Khurshid, Hasnat; Debie, Essam; Haider, Waqas; Wahab, Abdul

doi:10.3390/electronics9071177

Cited by 189 publications

(130 citation statements)

References 177 publications

(239 reference statements)

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“…erefore, most of the domestic Internet of ings is a wired monitoring system, and the technical approach is active FIRD combined with communication methods such as Ethernet [5]. In order to effectively respond to cyber threats, various research institutions and functional departments have strengthened their protection efforts [6], building a single-point defense system [7]. However, these defense tools often only focus on the partial information of cyber threats and timely and accurately detect threat behaviors and their internal associations, resulting in large deviations in detection results and formulating security strategies for network administrators [8].…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Internet of Things Security Detection Technology Based on Grey Association Decision Algorithm

et al. 2021

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This paper analyzes the current real-time monitoring system based on grey-related IoT security sensors for the detection of risk factors in the production environment of the Internet of Things and proposes a design plan for the Internet of Things environment monitoring based on the grey-related Internet of Things security sensor network, and according to the reliability guarantee mechanism of the system, a three-dimensional uniform IoT node deployment method suitable for IoT security monitoring is proposed. Based on the grey correlation analysis, it can provide a quantitative measurement analysis for the development and change state of a system, which is very suitable for the analysis of dynamic operating systems. As a real-time dynamic system of the Internet of Things, the use of grey correlation method to analyze its network security status has good operability and practical value. According to the multisource information processing technology, the monitoring data are preprocessed by dynamic limiting filtering, and then the data are fused at the data level with the optimal weighting algorithm. Through the use of grey correlation analysis to quantify the relative impact of cyberattacks on the network within a certain period of time, the quantitative assessment of the security environment and status of the entire network is realized. Finally, the characteristics of grey relational analysis and rough set theory attribute reduction are used to form the basis of grey correlation decision-level fusion algorithm, to achieve effective processing of the data of the Internet of Things security monitoring system.

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Section: Introductionmentioning

confidence: 99%

[Retracted] Internet of Things Security Detection Technology Based on Grey Association Decision Algorithm

et al. 2021

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“…They surveyed more than 95 papers in the literature that applied machine learning to deal with the issue of IoT intrusion detection. Another recent review presents and analyzes different machine learning and deep learning-based methods in order to identify the intrusion activities of IoT applications [46]. Both reviews emphasize the advantage of machine learning techniques against other approaches in intrusion detection problems.…”

Section: Related Workmentioning

confidence: 99%

A Multi-Stage Classification Approach for IoT Intrusion Detection Based on Clustering with Oversampling

et al. 2021

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Intrusion detection of IoT-based data is a hot topic and has received a lot of interests from researchers and practitioners since the security of IoT networks is crucial. Both supervised and unsupervised learning methods are used for intrusion detection of IoT networks. This paper proposes an approach of three stages considering a clustering with reduction stage, an oversampling stage, and a classification by a Single Hidden Layer Feed-Forward Neural Network (SLFN) stage. The novelty of the paper resides in the technique of data reduction and data oversampling for generating useful and balanced training data and the hybrid consideration of the unsupervised and supervised methods for detecting the intrusion activities. The experiments were evaluated in terms of accuracy, precision, recall, and G-mean and divided into four steps: measuring the effect of the data reduction with clustering, the evaluation of the framework with basic classifiers, the effect of the oversampling technique, and a comparison with basic classifiers. The results show that SLFN classification technique and the choice of Support Vector Machine and Synthetic Minority Oversampling Technique (SVM-SMOTE) with a ratio of 0.9 and the k value of 3 for k-means++ clustering technique give better results than other values and other classification techniques.

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“…Further, the readers can refer these literature [31], [32], [33] to understand the challenges, solutions and future directions in applying deep learning approaches for IDS within IoT.…”

Section: A Applied For Securing Iot Networkmentioning

confidence: 99%

Deep Learning Approaches for Intrusion Detection in IIoT Networks – Opportunities and Future Directions

Vaiyapuri¹,

Sbaï²,

Alaskar³

et al. 2021

IJACSA

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In recent years, the Industrial Internet of things (IIoT) is a fastest advancing innovative technology with a potential to digitize and interconnect many industries for huge business opportunities and development of global GDP. IIoT is used in diverse range of industries such as manufacturing, logistics, transportation, oil and gas, mining and metals, energy utilities and aviation. Although IIoT provides promising opportunities for the development of different industrial applications, they are prone to cyberattacks and demands for higher security requirements. The enormous number of sensors present in the IIoT network generates a large amount of data and has attracted the attention of cybercriminals across globe. The intrusion detection system (IDS) that monitors the network traffic and detects the behaviour of the network is considered as one of the key security solution for securing IIoT application from attacks. Recently, the application of machine and deep learning techniques have proved to mitigate multiple security threats and enhance the performance of intrusion detection. In this paper, we present a survey of deep learning-based IDS technique for IIoT. The main objective of this research is to provide the various deep learning-based IDS detection methods, datasets and comwparative analysis. Finally, this research aims to identify the limitations and challenges of existing studies, solutions and future directions.

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A Review of Intrusion Detection Systems Using Machine and Deep Learning in Internet of Things: Challenges, Solutions and Future Directions

Cited by 189 publications

References 177 publications

[Retracted] Internet of Things Security Detection Technology Based on Grey Association Decision Algorithm

[Retracted] Internet of Things Security Detection Technology Based on Grey Association Decision Algorithm

A Multi-Stage Classification Approach for IoT Intrusion Detection Based on Clustering with Oversampling

Deep Learning Approaches for Intrusion Detection in IIoT Networks – Opportunities and Future Directions

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