A Novel Wireless Network Intrusion Detection Method Based on Adaptive Synthetic Sampling and an Improved Convolutional Neural Network

Hu, Zhiquan; Wang, Liejun; Lei, Qi; Li, Yongming; Chen, Danny

doi:10.1109/access.2020.3034015

Cited by 60 publications

(33 citation statements)

References 33 publications

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“…However, according to the thyristor operation data obtained in the field test, the dataset shows typical inter-class imbalance characteristics, i.e., the amount of aging data is extremely small compared to the monitoring data under normal states. The problem of inter-class data imbalance has a great impact on the accuracy of data-driven thyristor state evaluation and tends to lead to inefficient algorithms [11]. Therefore, the inter-class imbalance needs to be solved to obtain the interclass tensor domain classification surfaces by training effectively.…”

Section: Introductionmentioning

confidence: 99%

RETRACTED: Thyristor Aging-State-Evaluation Method Based on State Information and Tensor Domain Theory

Lei

Guo

Tian³

et al. 2021

Electronics

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The thyristor is the key device for the converter of the ultra-high-voltage DC (UHVDC) project to realize AC–DC conversion. The reliability of thyristors is directly related to the safe operation of the UHVDC transmission system. Due to the complex operating environment of the thyristor, there are many interrelated parameters that may affect the aging state of thyristors. To extract useful information from the massive high-dimensional data and further obtain the aging state of thyristors, a supervised tensor domain classification (STDC) method based on the adaptive syn-thetic sampling method, the gradient-boosting decision tree, and tensor domain theory is proposed in this paper. Firstly, the algorithm applies the continuous medium theory to analogize the aging state points of the thyristor to the mass points in the continuous medium. Then, the algorithm applies the concept of the tensor domain to identify the aging state of the thyristor and to transform the original state-identification problem into the state classification surface determination of the tensor domain. Secondly, a temporal fuzzy clustering algorithm is applied to realize automatic positioning of the classification surface of each tensor sub-domain. Furthermore, to solve the problem of unbalanced sample size between aging class data and normal class data in the state-identification domain, the improved adaptive synthetic sampling algorithm is applied to preprocess the data. The gradient-boosting decision tree algorithm is applied to solve the multi-classification problem of the thyristor. Finally, the comparison between the algorithm proposed and the conventional algorithm is performed through the field-test data provided by the CSG EHV Power Transmission Company of China’s Southern Power Grid. It is verified that the evaluation method proposed has higher recognition accuracy and can effectively classify the thyristor states.

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Section: Introductionmentioning

confidence: 99%

RETRACTED: Thyristor Aging-State-Evaluation Method Based on State Information and Tensor Domain Theory

Lei

Guo

Tian³

et al. 2021

Electronics

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“…When additional attributes were added for the experiment, the time complexity gradually increased, but the intrusion detection results did not change significantly, so the first 16 attributes were selected in this paper. 11 Wireless Communications and Mobile Computing performance evaluation indexes of wireless network intrusion detection algorithm [29]. The details are as follows:…”

Section: Pca Methods For Dimensionality Reduction Of Wirelessmentioning

confidence: 99%

An Improved K‐Means Clustering Intrusion Detection Algorithm for Wireless Networks Based on Federated Learning

Xie

Dong

Wang

2021

Wireless Communications and Mobile Computing

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The existing wireless network intrusion detection algorithms based on supervised learning confront many challenges, such as high false detection rate, difficulty in finding unknown attack behaviors, and high cost in obtaining labeled training data sets. This paper presents an improved k -means clustering algorithm for detecting intrusions on wireless networks based on Federated Learning. The proposed algorithm allows multiple participants to train a global model without sharing their private data and can expand the amount of data in the training model and protect the local data of each participant. Furthermore, the cosine distance of multiple perspectives is introduced in the algorithm to measure the similarity between network data objects in the improved k -means clustering process, making the clustering results more reasonable and the judgment of network data behavior more accurate. The AWID, an open wireless network attack data set, is selected as the experimental data set. Its dimensionality reduces by the method of principal component analysis (PCA). Experimental results show that the improved k -means clustering intrusion detection algorithm based on Federated Learning has better performance in detection rate, false detection rate, and detection of unknown attack types.

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“…DNN [20] 77.75 % -PSO-ANN [21] 88.90 % -GA-ANN [21] 83.11 % -BAT [22] 82.56 % -BAT-MC [22] 84.25 % -MuliTree [23] 85.20 % -AE-DNN [24] -79.74% AS-CNN [25] -80.00% TSE-IDS [26] 85.79% -BGSA-MI [27] 88.36% -RelieF [27] 84.60% -DE-ELM [28] 87.83% 80.15% Filter-SVM [29] -77.17% Wrapper-SVM [29] -79.65% GA-DT (Proposed)(v 3 ) 89.26% -GA-XGB (Proposed)(g 2 ) -87.26%…”

Section: Model Binary Accuracy Multiclass Accuracymentioning

confidence: 99%

“…In contrast to the work presented 403 in [24], the GA-XGB ameliorated by 7.52 %. With regards to the AS-CNN in [25], the 404 GA-XGB improved by 7.26 %. Moreover, in contrast to the DE-ELM in [28], Filter-SVM 405 in [29] and the Wrapper-SVM in [29]; the TAC of the GA-XGB improved its performance 406 by 7.11 %, 10.09 %, an 7.61 %, respectively.…”

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confidence: 99%

Genetic Algorithm Based Feature Selection Technique for Optimal Intrusion Detection

Kasongo¹

2021

Preprint

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In recent years, several industries have registered an impressive improvement in technological advances such as Internet of Things (IoT), e-commerce, vehicular networks, etc. These advances have sparked an increase in the volume of information that gets transmitted from different nodes of a computer network (CN). As a result, it is crucial to safeguard CNs against security threats and intrusions that can compromise the integrity of those systems. In this paper, we propose a machine mearning (ML) intrusion detection system (IDS) in conjunction with the Genetic Algorithm (GA) for feature selection. To assess the effectiveness of the proposed framework, we use the NSL-KDD dataset. Furthermore, we consider the following ML methods in the modelling process: decision tree (DT), support vector machine (SVM), random forest (RF), extra-trees (ET), extreme gradient boosting (XGB), and naïve Bayes (NB). The results demonstrated that using the GA algorithm has a positive impact on the performance of the selected classifiers. Moreover, the results obtained by the proposed ML methods were superior to existing methodologies.

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A Novel Wireless Network Intrusion Detection Method Based on Adaptive Synthetic Sampling and an Improved Convolutional Neural Network

Cited by 60 publications

References 33 publications

RETRACTED: Thyristor Aging-State-Evaluation Method Based on State Information and Tensor Domain Theory

RETRACTED: Thyristor Aging-State-Evaluation Method Based on State Information and Tensor Domain Theory

An Improved K‐Means Clustering Intrusion Detection Algorithm for Wireless Networks Based on Federated Learning

Genetic Algorithm Based Feature Selection Technique for Optimal Intrusion Detection

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