SPIDER: A shallow PCA based network intrusion detection system with enhanced recurrent neural networks

Udas, Pritom Biswas; Karim, Md. Ebtidaul; Roy, Kowshik Sankar

doi:10.1016/j.jksuci.2022.10.019

Cited by 27 publications

(8 citation statements)

References 33 publications

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“…Abdulkareem et al [19] identified the most important features before applying PCA for feature reduction, resulting in a reduced feature set from 36 to 5 while maintaining classification accuracy on the Bot-IoT dataset. Udas et al [20] constructed a hybrid recurrent neural network (RNN) model, adopting PCA as the feature reduction method. T experimental results showed that the model performed well on the NSL-KDD and UNSW-NB15 datasets in anomaly detection.…”

Section: A Automatic Feature Extractionmentioning

confidence: 99%

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Research on Dung Beetle Optimization Based Stacked Sparse Autoencoder for Network Situation Element Extraction

Yang,

Zhao

2024

IEEE Access

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Network security situation awareness enables networks to actively and effectively defend against network attacks, relying on the extraction of network situation elements as an initial and decisive step. In existing studies, the stacked sparse autoencoder (SSAE) has been employed to extract features from unlabeled network flows. However, obtaining the optimal hyperparameter combination is challenging due to its numerous hyperparameters. To address this issue, we propose a novel approach named DBO-SSAE that leverages dung beetle optimization (DBO) to select the optimal hyperparameters for SSAE automatically. Applied to the well-known UNSW-NB15 dataset, our model yields an optimal feature subset, which is evaluated across various binary classifiers with different metrics. Experimental results demonstrate that our approach improves accuracy and F1-measure by 0.2% to 1.5%, while reducing the false negative rate (FNR) and false positive rate (FPR) by 0.06% to 7%, surpassing other feature extraction methods on the same classifier for the UNSW-NB15 dataset. Particularly, in conjunction with a lightweight bidirectional long short-term memory (BiLSTM), our model achieves metrics of 98.84% accuracy, 98.96% F1-measure, 1.86% FNR, and 0.6% FPR. This study could provide novel insights into the effective representation of network situation elements and lay the groundwork for a high-efficiency intrusion detection system.

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Section: A Automatic Feature Extractionmentioning

confidence: 99%

“…If a better feature subset is to be obtained, these parameters should be set prudently. [33]` 98.33% 2019 FA-AE-BNP [65] 92.84% 2021 SPIDER [20] 86.28% 2022 GA-1D-CNN [53] 98% 2023 SIGMOD [34] 96.07% 2023 GRU-BWFA [66] 98.79% 2023 MF-Net [67] 91.4% 2024 proposed DBO-SSAE-BiLSTM 98.96% 2024…”

Section: B Numerous Parameters Of Dbo Itselfmentioning

confidence: 99%

Research on Dung Beetle Optimization Based Stacked Sparse Autoencoder for Network Situation Element Extraction

Yang,

Zhao

2024

IEEE Access

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“…A novel technique has been suggested to improve the precision and detection rate of network-based intrusion detection systems (NIDS) for identifying anomalies while reducing the incidence of false positives [20]. The proposed approach utilizes a combination of artificial bee colony (ABC) algorithm for feature selection and the AdaBoost algorithm for the assessment and classification of features.…”

Section: Related Workmentioning

confidence: 99%

“…1,2,3,4,5,7,8,9,11,12,14,15,16,17,20,23, 28, 29, 30, 31, 32, 34, 35, 36, 38, 45, 47, 49, 52, 54, 55, 57, 58, 59 0.9975 2, 3, 4, 5, 7, 11, 14, 16, 17, 18, 21, 23, 25, 27, 29, 30, 31, 32, 33, 34, 36, 39, 41, 43, 44, 45, 48, 49, 50, 52, 53, 55, 57, 58, 59 0.9977 1, 2, 3, 4, 7, 11, 15, 16, 17, 18, 19, 21, 22, 25, 28, 29, 30, 33, 34, 38, 41, 43, 44, 48, 49, 50, 52, 53, 55, 57, 58, 59 0.9982…”

mentioning

confidence: 99%

Enhancing Intrusion Detection in Industrial Internet of Things through Automated Preprocessing

Sezgin¹,

Boyacı²

2023

Adv. Sci. Technol. Res. J.

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Industrial Internet of Things (IIoT) is a rapidly growing field, where interconnected devices and systems are used to improve operational efficiency and productivity. However, the extensive connectivity and data exchange in the IIoT environment make it vulnerable to cyberattacks. Intrusion detection systems (IDS) are used to monitor IIoT networks and identify potential security breaches. Feature selection is an essential step in the IDS process, as it can reduce computational complexity and improve the accuracy of the system. In this research paper, we propose a hybrid feature selection approach for intrusion detection in the IIoT environment using Shapley values and a genetic algorithm-based automated preprocessing technique which has three automated steps including imputation, scaling and feature selection. Shapley values are used to evaluate the importance of features, while the genetic algorithm-based automated preprocessing technique optimizes feature selection. We evaluate the proposed approach on a publicly available dataset and compare its performance with existing state-of-the-art methods. The experimental results demonstrate that the proposed approach outperforms existing methods, achieving high accuracy, precision, recall, and F1-score. The proposed approach has the potential to enhance the performance of IDS in the IIoT environment and improve the overall security of critical industrial systems.

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“…5. Compared to a regular RNN block, a typical GRU cell has some additional benefits, which include its increased functionality of handling long-term data sequences and improved operation execution of temporal attributes [20]. Therefore, a GRU block has been added to our neural network in order to more easily and effectively extract the signal features from the dataset.…”

Section: Gru and Bi-gru Layersmentioning

confidence: 99%

An RNN-based Hybrid Model for Classification of Electrooculogram Signal for HCI

Roy,

Islam

2023

IJC

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In recent years, there has been a rise in the amount of research conducted in the field of human-computer interaction (HCI) employing electrooculography (EOG), which is a technology that is effectively and widely used to detect human eye activity. The use of EOG signals as a control signal for HCI is essential for understanding, characterizing, and classifying eye movements, which can be applied to a wide range of applications including virtual mouse and keyboard control, electric power wheelchairs, industrial assistive robots, and patient rehabilitation or communication purposes. In the field of HCI, EOG signals classification has continuously been performed to make the system more effective and reliable than ever. In this paper, a Recurrent neural network model is proposed for classifying eye movement directions utilizing several informative feature extraction methods and noise filtering. Our classification model is comprised of Gated Recurrent Unit (GRU) with a Bidirectional GRU followed by dense layers. The classifier is investigated to find a better classification performance of four directional eye movements: Up and Down for the vertical channel, along with Left and Right for the horizontal channel of EOG signals. The classifier achieved 99.77% and 99.74% accuracy for vertical and horizontal channels, respectively, which outperforms the compared state-of-the-art studies. The proposed classifier allows disabled people to make life-improving decisions using computers, achieving the highest classification performance for rehabilitation and other applications.

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SPIDER: A shallow PCA based network intrusion detection system with enhanced recurrent neural networks

Cited by 27 publications

References 33 publications

Research on Dung Beetle Optimization Based Stacked Sparse Autoencoder for Network Situation Element Extraction

Research on Dung Beetle Optimization Based Stacked Sparse Autoencoder for Network Situation Element Extraction

Enhancing Intrusion Detection in Industrial Internet of Things through Automated Preprocessing

An RNN-based Hybrid Model for Classification of Electrooculogram Signal for HCI

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