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 57 publications

(33 citation statements)

References 33 publications

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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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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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“…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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“…To avoid the model becoming sensitive to big samples while remaining insensitive to small samples, Hu et al [15] utilise the ADASYN technique. An improved CNN has been created using the split convolution module (SPC-CNN), which increases feature variety while simultaneously decreasing the impact of redundant interchange information on model training.…”

Section: Literature Reviewmentioning

confidence: 99%

Comparison of Classification Model for the Detection of Cyber-attack using Ensemble Learning Models

Akhtar¹,

Tao²

2018

ICST Transactions on Scalable Information Systems

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Incorporating digital technologies into security systems is a positive development. It's time for the digital system to be appropriately protected from potential threats and attacks. An intrusion detection system can identify both external and internal anomalies in the network. There are a variety of threats out there, both active and passive. If these dangers aren't addressed, attacks and data theft could occur from the point of origin all the way to the point of destination. Machine learning is still in its infancy, despite its wide range of applications. It is possible to predict the future by using machine learning. A cyber-attack detection system is depicted in this study using machine learning models. Machine learning algorithms were trained to predict cyber-attack scores using data from prior cyber-attacks on an open source website. In order to detect an attack at its earliest possible stage, this research also examined multiple linear machine learning algorithm-based categorization models. Classifiers' accuracy is also compared in the presentation, as is the presentation itself. Balance procedures were followed. Radio Frequency and GBC have the best accuracy, at 87.93%, followed by ABC at 86.11%, BT at 81.03%, ET at 70.31%, and DT at 70.31 percent (84.48 percent ).

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

Cited by 57 publications

References 33 publications

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

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

Comparison of Classification Model for the Detection of Cyber-attack using Ensemble Learning Models

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