Internet of Things Intrusion Detection System Based on Convolutional Neural Network

Yin, Jie; Shi, Yuxuan; Dong, Wen; Chen, Yin; Wang, Tiannan; Song, Yong; Li, Tianyao; Li, Yicheng

doi:10.32604/cmc.2023.035077

Cited by 2 publications

(1 citation statement)

References 33 publications

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“…The application of machine learning techniques in IDS has become an active area of research in recent years. Researchers aim to develop new and improved methods to improve the performance of IDS in detecting and preventing network attacks [7]. The objective is to enhance security against cyber-attacks and secure sensitive data by utilizing machine learning technology [8].…”

Section: Introductionmentioning

confidence: 99%

AID4I: An Intrusion Detection Framework for Industrial Internet of Things Using Automated Machine Learning

Sezgin,

Boyacı

2023

Computers, Materials &Amp; Continua

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By identifying and responding to any malicious behavior that could endanger the system, the Intrusion Detection System (IDS) is crucial for preserving the security of the Industrial Internet of Things (IIoT) network. The benefit of anomaly-based IDS is that they are able to recognize zeroday attacks due to the fact that they do not rely on a signature database to identify abnormal activity. In order to improve control over datasets and the process, this study proposes using an automated machine learning (AutoML) technique to automate the machine learning processes for IDS. Our groundbreaking architecture, known as AID4I, makes use of automatic machine learning methods for intrusion detection. Through automation of preprocessing, feature selection, model selection, and hyperparameter tuning, the objective is to identify an appropriate machine learning model for intrusion detection. Experimental studies demonstrate that the AID4I framework successfully proposes a suitable model. The integrity, security, and confidentiality of data transmitted across the IIoT network can be ensured by automating machine learning processes in the IDS to enhance its capacity to identify and stop threatening activities. With a comprehensive solution that takes advantage of the latest advances in automated machine learning methods to improve network security, AID4I is a powerful and effective instrument for intrusion detection. In preprocessing module, three distinct imputation methods are utilized to handle missing data, ensuring the robustness of the intrusion detection system in the presence of incomplete information. Feature selection module adopts a hybrid approach that combines Shapley values and genetic algorithm. The Parameter Optimization module encompasses a diverse set of 14 classification methods, allowing for thorough exploration and optimization of the parameters associated with each algorithm. By carefully tuning these parameters, the framework enhances its adaptability and accuracy in identifying potential intrusions. Experimental results demonstrate that the AID4I framework can achieve high levels of accuracy in detecting network intrusions up to 14.39% on public datasets, outperforming traditional 2122 CMC, 2023, vol.76, no.2 intrusion detection methods while concurrently reducing the elapsed time for training and testing.

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Section: Introductionmentioning

confidence: 99%

AID4I: An Intrusion Detection Framework for Industrial Internet of Things Using Automated Machine Learning

Sezgin,

Boyacı

2023

Computers, Materials &Amp; Continua

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A multi-classification detection model for imbalanced data in NIDS based on reconstruction and feature matching

Yang,

Cheng,

Liu

et al. 2024

J Cloud Comp

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With the exponential growth of various data interactions on network systems, network intrusions are also increasing. The emergence of edge computing technology brings a new solution to network security. However, due to the difficulty of processing massive and unbalanced data at the edge, higher accuracy requirements are necessary for deployed detection models. This paper proposes a multi-classification model for network intrusion detection based on reconstruction and feature matching. This model can be deployed on small-scale edge nodes, effectively identifying various attack behaviors through the utilization of reconstruction errors and adaptive scaling. Furthermore, we proposed a model transfer method based on feature matching to enhance the training and detection efficiency of multi-classification models under different data distribution conditions. The proposed model has been evaluated on the CICIDS2017 dataset in terms of accuracy, recall, precision and F1 score. The model demonstrates high accuracy for normal flows in the network, majority class attacks, and minority class attacks, achieving an overall multi-class accuracy of 99.81%, outperforming similar models. Furthermore, this model demonstrates faster convergence and training speed after feature matching, exhibiting better robustness and outstanding performance at the edge.

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Internet of Things Intrusion Detection System Based on Convolutional Neural Network

Cited by 2 publications

References 33 publications

AID4I: An Intrusion Detection Framework for Industrial Internet of Things Using Automated Machine Learning

AID4I: An Intrusion Detection Framework for Industrial Internet of Things Using Automated Machine Learning

A multi-classification detection model for imbalanced data in NIDS based on reconstruction and feature matching

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