A Feature Selection Approach for Network Intrusion Detection Based on Tree-Seed Algorithm and K-Nearest Neighbor

Chen, Feng; Ye, Zhiwei; Wang, Chunzhi; Yan, Lingyu; Wang, Ruoxi

doi:10.1109/idaacs-sws.2018.8525522

Cited by 44 publications

(18 citation statements)

References 6 publications

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“…After obtaining the weights of each criterion as shown in Table 13, the second phases rank alternative data using the TOPSIS method. The initial steps of the TOPSIS method create the alternative data normalization matrix using (18) and create the Weighted Alternative Data Normalization Matrix using (19). The results of the Weighted Alternative Data Normalization Matrix are used to calculate the values of Positive Ideal Solutions Matrix and Negative Ideal Solutions Matrix using (20) as shown in Table 14.…”

Section: Resultsmentioning

confidence: 99%

“…K-Nearest Neighbor is a supervised algorithm learning based on the k-nearest neighbor by classifying new instances based on the majority of the k-nearest neighbor categories [18]. This method calculates similarities between samples of unlabeled data and all training data samples [19]. KNN is determined by looking at the shortest distance from the query instance to the training sample data.…”

Section: Classification Processmentioning

confidence: 99%

“…MCDM was developed to provide solutions in the decision making the process [45]. There are five steps in the TOPSIS method [46,47] (18) Step 2: Calculate the weighted normalized decision matrix (19).…”

Section: Figure 3 Hierarchy Of Purpose Selection Best Algorithmmentioning

confidence: 99%

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Data mining, fuzzy AHP and TOPSIS for optimizing taxpayer supervision

Jupri

Sarno

2020

IJEECS

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The achievement of accepting optimal tax need effective and efficient tax supervision can be achieved by classifying taxpayer compliance to tax regulations. Considering this issue, this paper proposes the classification of taxpayer compliance using data mining algorithms; i.e. C4.5, Support Vector Machine, K-Nearest Neighbor, Naive Bayes, and Multilayer Perceptron based on the compliance of taxpayer data. The taxpayer compliance can be classified into four classes, which are (1) formal and material compliant taxpayers, (2) formal compliant taxpayers, (3) material compliant taxpayers, and (4) formal and material non-compliant taxpayers. Furthermore, the results of data mining algorithms are compared by using Fuzzy AHP and TOPSIS to determine the best performance classification based on the criteria of Accuracy, F-Score, and Time required. Selection of the taxpayer's priority for more detailed supervision at each level of taxpayer compliance is ranked using Fuzzy AHP and TOPSIS based on criteria of dataset variables. The results show that C4.5 is the best performance classification and achieves preference value of 0.998; whereas the MLP algorithm results from the lowest preference value of 0.131. Alternative taxpayer A233 is the top priority taxpayer with a preference value of 0.433; whereas alternative taxpayer A051 is the lowest priority taxpayer with a preference value of 0.036.

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Section: Resultsmentioning

confidence: 99%

Section: Classification Processmentioning

confidence: 99%

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Data mining, fuzzy AHP and TOPSIS for optimizing taxpayer supervision

Jupri

Sarno

2020

IJEECS

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“…The proposed method is able to identify features that can improve the accuracy of attack detection. Chen et al [25] introduce a tree-seed algorithm (TSA) that is used to extract effective features. The proposed algorithm reduces the dimension of data, by eliminating redundant features, which in turn improve the accuracy of the K-Nearest Neighbor (KNN) classifier.…”

Section: Relevant Researchesmentioning

confidence: 99%

CICIDS-2017 Dataset Feature Analysis With Information Gain for Anomaly Detection

et al. 2020

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Feature selection (FS) is one of the important tasks of data preprocessing in data analytics. The data with a large number of features will affect the computational complexity, increase a huge amount of resource usage and time consumption for data analytics. The objective of this study is to analyze relevant and significant features of huge network traffic to be used to improve the accuracy of traffic anomaly detection and to decrease its execution time. Information Gain is the most feature selection technique used in Intrusion Detection System (IDS) research. This study uses Information Gain, ranking and grouping the features according to the minimum weight values to select relevant and significant features, and then implements Random Forest (RF), Bayes Net (BN), Random Tree (RT), Naive Bayes (NB) and J48 classifier algorithms in experiments on CICIDS-2017 dataset. The experiment results show that the number of relevant and significant features yielded by Information Gain affects significantly the improvement of detection accuracy and execution time. Specifically, the Random Forest algorithm has the highest accuracy of 99.86% using the relevant selected features of 22, whereas the J48 classifier algorithm provides an accuracy of 99.87% using 52 relevant selected features with longer execution time.

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“…In recent years, machine learning has been widely used to identify various types of network attacks in NIDS, thereby helping administrators take appropriate measures to prevent network intrusions. However, most traditional machine learning methods are shallow learning techniques, such as KNN (k-Nearest Neighbor) [4], SVM (Support Vector Machine) [5], SOM (Self-Organizing Map) [6] and so on. There are many problems in shallow learning methods, including a heavy dependence on feature engineering and feature selection, poor ability to detect unknown network attacks and high false alarm rates.…”

Section: Introductionmentioning

confidence: 99%

Network Intrusion Detection Based on Supervised Adversarial Variational Auto-Encoder With Regularization

et al. 2020

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To explore the advantages of adversarial learning and deep learning, we propose a novel network intrusion detection model called SAVAER-DNN, which can not only detect known and unknown attacks but also improve the detection rate of low-frequent attacks. SAVAER is a supervised variational auto-encoder with regularization, which uses WGAN-GP instead of the vanilla GAN to learn the latent distribution of the original data. SAVAER's decoder is used to synthesize samples of low-frequent and unknown attacks, thereby increasing the diversity of training samples and balancing the training data set. SAVAER's encoder is used to initialize the weights of the hidden layers of the DNN and explore high-level feature representations of the original samples. The benchmark NSL-KDD (KDDTest+), NSL-KDD (KDDTest-21) and UNSW-NB15 datasets are used to evaluate the performance of the proposed model. The experimental results show that the proposed SAVAER-DNN is more suitable for data augmentation than the other three well-known data oversampling methods. Moreover, the proposed SAVAER-DNN outperforms eight well-known classification models in detection performance and is more effective in detecting low-frequent and unknown attacks. Furthermore, compared with other state-of-the-art intrusion detection models reported in the IDS literature, the proposed SAVAER-DNN offers better performance in terms of overall accuracy, detection rate, F1 score, and false positive rate.

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A Feature Selection Approach for Network Intrusion Detection Based on Tree-Seed Algorithm and K-Nearest Neighbor

Cited by 44 publications

References 6 publications

Data mining, fuzzy AHP and TOPSIS for optimizing taxpayer supervision

Data mining, fuzzy AHP and TOPSIS for optimizing taxpayer supervision

CICIDS-2017 Dataset Feature Analysis With Information Gain for Anomaly Detection

Network Intrusion Detection Based on Supervised Adversarial Variational Auto-Encoder With Regularization

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