A Review on Machine Learning and Deep Learning Perspectives of IDS for IoT: Recent Updates, Security Issues, and Challenges

Thakkar, Ankit; Lohiya, Ritika

doi:10.1007/s11831-020-09496-0

Cited by 187 publications

(77 citation statements)

References 144 publications

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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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“…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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“…Here, the algorithms are able to learn representations to predict cases that are not known, from labelled inputted data. Examples of supervised machine learning algorithms include SVMs for solving issues regarding classification and RFs for solving both regression and classification issues [64,70]. SVMs are extensively used in IDS-based research as a result of their powerful classifying and their computing practicality.…”

Section: Supervised Learningmentioning

confidence: 99%

“…In the unsupervised learning scheme, representations as well as the structure from inputted data that are not labelled are learned by the algorithms. The aim of this algorithm is the modeling of foundational data structures for the prediction of data that are not known [64,70]. Some examples of these unsupervised learning algorithms include techniques used in reducing features such as clustering techniques, PCAs, and Self-Organizational Maps (SOMs).…”

Section: Unsupervised Learningmentioning

confidence: 99%

Classifier Performance Evaluation for Lightweight IDS Using Fog Computing in IoT Security

et al. 2021

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In this article, a Host-Based Intrusion Detection System (HIDS) using a Modified Vector Space Representation (MVSR) N-gram and Multilayer Perceptron (MLP) model for securing the Internet of Things (IoT), based on lightweight techniques and using Fog Computing devices, is proposed. The Australian Defence Force Academy Linux Dataset (ADFA-LD), which contains exploits and attacks on various applications, is employed for the analysis. The proposed method is divided into the feature extraction stage, the feature selection stage, and classification modeling. To maintain the lightweight criteria, the feature extraction stage considers a combination of 1-gram and 2-gram for the system call encoding. In addition, a Sparse Matrix is used to reduce the space by keeping only the weight of the features that appear in the trace, thus ignoring the zero weights. Subsequently, Linear Correlation Coefficient (LCC) is utilized to compensate for any missing N-gram in the test data. In the feature selection stage, the Mutual Information (MI) method and Principle Component Analysis (PCA) are utilized and then compared to reduce the number of input features. Following the feature selection stage, the modeling and performance evaluation of various Machine Learning classifiers are conducted using a Raspberry Pi IoT device. Further analysis of the effect of MLP parameters, such as the number of nodes, number of features, activation, solver, and regularization parameters, is also conducted. From the simulation, it can be seen that different parameters affect the accuracy and lightweight evaluation. By using a single hidden layer and four nodes, the proposed method with MI can achieve 96% accuracy, 97% recall, 96% F1-Measure, 5% False Positive Rate (FPR), highest curve of Receiver Operating Characteristic (ROC), and 96% Area Under the Curve (AUC). It also achieved low CPU time usage of 4.404 (ms) milliseconds and low energy consumption of 8.809 (mj) millijoules.

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“…While the IoT will make life easier for humans, the aspect of data protection is of great concern (Thakkar & Lohiya, 2021;Mahboub, Ahmed & Saeed, 2021). IoT platform has been a popular target for cybercriminals as it faces significant risks.…”

Section: Research Areamentioning

confidence: 99%

IoT-based intrusion detection system using convolution neural networks

Aljumah

2021

PeerJ Computer Science

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In the Information and Communication Technology age, connected objects generate massive amounts of data traffic, which enables data analysis to uncover previously hidden trends and detect unusual network-load. We identify five core design principles to consider when designing a deep learning-empowered intrusion detection system (IDS). We proposed the Temporal Convolution Neural Network (TCNN), an intelligent model for IoT-IDS that aggregates convolution neural network (CNN) and generic convolution, based on these concepts. To handle unbalanced datasets, TCNN is accumulated with synthetic minority oversampling technique with nominal continuity. It is also used in conjunction with effective feature engineering techniques like attribute transformation and reduction. The presented model is compared to two traditional machine learning algorithms, random forest (RF) and logistic regression (LR), as well as LSTM and CNN deep learning techniques, using the Bot-IoT data repository. The outcomes of the experiments depicts that TCNN maintains a strong balance of efficacy and performance. It is better as compared to other deep learning IDSs, with a multi-class traffic detection accuracy of 99.9986 percent and a training period that is very close to CNN.

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A Review on Machine Learning and Deep Learning Perspectives of IDS for IoT: Recent Updates, Security Issues, and Challenges

Cited by 187 publications

References 144 publications

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

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

Classifier Performance Evaluation for Lightweight IDS Using Fog Computing in IoT Security

IoT-based intrusion detection system using convolution neural networks

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