A classification model based on svm and fuzzy rough set for network intrusion detection

Kejia, Shen; Parvïn, Hamïd; Qasem, Sultan Noman; Tuan, Bui Anh; Pho, Kim-Hung

doi:10.3233/jifs-191621

Cited by 5 publications

(4 citation statements)

References 64 publications

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“…Recently, authors in [26], used fuzzy rough sets theory (FRST) for prototype selection to enhance SVM in intrusion detection system (IDS). Their experimental results show that the proposed technique provides better generalization in terms of precision, recall, and accuracy rate.…”

Section: Fuzzy Rough Set Based Feature Selection Methodsmentioning

confidence: 99%

An Efficient Classification Model using Fuzzy Rough Set Theory and Random Weight Neural Network

Raza

2021

Research Journal Of Computer Science And Information Technology

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In the area of fuzzy rough set theory (FRST), researchers have gained much interest in handling the high-dimensional data. Rough set theory (RST) is one of the important tools used to pre-process the data and helps to obtain a better predictive model, but in RST, the process of discretization may loss useful information. Therefore, fuzzy rough set theory contributes well with the real-valued data. In this paper, an efficient technique is presented based on Fuzzy rough set theory (FRST) to pre-process the large-scale data sets to increase the efficacy of the predictive model. Therefore, a fuzzy rough set-based feature selection (FRSFS) technique is associated with a Random weight neural network (RWNN) classifier to obtain the better generalization ability. Results on different dataset show that the proposed technique performs well and provides better speed and accuracy when compared by associating FRSFS with other machine learning classifiers (i.e., KNN, Naive Bayes, SVM, decision tree and backpropagation neural network).

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Section: Fuzzy Rough Set Based Feature Selection Methodsmentioning

confidence: 99%

An Efficient Classification Model using Fuzzy Rough Set Theory and Random Weight Neural Network

Raza

2021

Research Journal Of Computer Science And Information Technology

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“…(4) Decision discrimination, in this stage, the intrusion detection system discriminates the decision on the detection results. The trained one is deployed on the test dataset to be detected or deployed in the network environment to discriminate the intrusion detection results, identify the intrusion events, and respond [11][12].…”

Section: Intrusion Detection Framework Based On Machine Learningmentioning

confidence: 99%

Intrusion Detection Classification Based on Random Forest

2022

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In recent years, network security has become increasingly severe and network intrusions are frequent. On the one hand, it is difficult to capture intrusions because of the increasingly large volume of computer networks and complex network topology, and on the other hand, it is more difficult to capture intrusions because the characteristics of intrusions are becoming more diverse and complex. The purpose of this paper is to study intrusion detection classification based on random forest. After constructing a high-precision decision tree, the double failure metric is used as the distance between two decision trees, and the k-means++ algorithm is used to select high-precision decision trees with a certain degree of independence to form the final random forest. The improved random forest algorithm for setting up clustering and the random forest algorithm were experimented as algorithm comparisons. The KDD-NSL dataset was selected and the experimental results were compared to demonstrate the effectiveness of the random forest algorithm based on gradient boosting. The experimental results show that the improved random forest model has a significant reduction in decision tree size and a significant increase in diversity due to the k-means++ algorithm for clustering and extracting cluster centers.

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“…Traditional methods include one-dimensional distance profile recognition based on Support Vector Machines (SVM) [7][8] , feature extraction using Gaussian Mixture Model (GMM) 9 , and so on. However, these methods rely on shallow linear structures and cannot fully extract features representing target information.…”

Section: Introductionmentioning

confidence: 99%

Ship HRRP target recognition method based on transformer and convolutional attention network

ZIJIE,

GUANGFEN,

et al. 2024

Fifteenth International Conference on Signal Processing Systems (ICSPS 2023)

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The radar's high-resolution range profile (HRRP) data structure is complex, and extracting stable and reliable features from it is crucial for HRRP target recognition. In this paper, we propose to use the convolution module to extract local spatial features of HRRP and use the positional encoding to embed the position information to generate new temporal features, and then capture the long-term dependency within the distance unit of HRRP through the multi-head new selfattention mechanism of the Transformer encoder, to construct a reliable feature extraction method for the HRRP target. Finally, a new deep learning model CNN-TEAN (CNN TransEncoder-Attention Network), based on one-dimensional residual convolution, Transformer encoder, and attention mechanism, is formed using the attention mechanism, fully connected layer, and softmax for classification. Using six simulated ship target data types for experimental validation, the CNN-TEAN model proposed in this paper can achieve a higher recognition rate than RNN, LSTM, and SVM models.

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A classification model based on svm and fuzzy rough set for network intrusion detection

Cited by 5 publications

References 64 publications

An Efficient Classification Model using Fuzzy Rough Set Theory and Random Weight Neural Network

An Efficient Classification Model using Fuzzy Rough Set Theory and Random Weight Neural Network

Intrusion Detection Classification Based on Random Forest

Ship HRRP target recognition method based on transformer and convolutional attention network

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