Extreme Gradient Boosting Based Tuning for Classification in Intrusion Detection Systems

Bansal, Ashu; Kaur, Sanmukh

doi:10.1007/978-981-13-1810-8_37

Cited by 57 publications

(29 citation statements)

References 10 publications

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“…Although the multi-class classification performance of our proposed method has been proven through experiments, to provide more reference for the readers, we still compare the results of our CFS-BA-Ensemble method with other earlier researches in binary classification based on NSL-KDD, AWID, and CIC-IDS2017 datasets, which is shown in Table 13. First of all, it can be seen in Table 13 that our proposed model outperforms other similar ensemble classifiers, such as FS-EL [83], XGBoost-IDS [13], and TSE-IDS [82] when using 10f cross-validation as a validation technique. There are also some deep learning methods for IDS in the current literature such as DEMISe [69], DeepWindow [79], and HELAD [99].…”

Section: Comparison With the State Of The Art Methodsmentioning

confidence: 86%

Building an efficient intrusion detection system based on feature selection and ensemble classifier

et al. 2020

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A B S T R A C T Intrusion detection system (IDS) is one of extensively used techniques in a network topology to safeguard the integrity and availability of sensitive assets in the protected systems. Although many supervised and unsupervised learning approaches from the field of machine learning have been used to increase the efficacy of IDSs, it is still a problem for existing intrusion detection algorithms to achieve good performance. First, lots of redundant and irrelevant data in high-dimensional datasets interfere with the classification process of an IDS. Second, an individual classifier may not perform well in the detection of each type of attacks. Third, many models are built for stale datasets, making them less adaptable for novel attacks. Thus, we propose a new intrusion detection framework in this paper, and this framework is based on the feature selection and ensemble learning techniques. In the first step, a heuristic algorithm called CFS-BA is proposed for dimensionality reduction, which selects the optimal subset based on the correlation between features. Then, we introduce an ensemble approach that combines C4.5, Random Forest (RF), and Forest by Penalizing Attributes (Forest PA) algorithms. Finally, voting technique is used to combine the probability distributions of the base learners for attack recognition. The experimental results, using NSL-KDD, AWID, and CIC-IDS2017 datasets, reveal that the proposed CFS-BA-Ensemble method is able to exhibit better performance than other related and state of the art approaches under several metrics.

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Section: Comparison With the State Of The Art Methodsmentioning

confidence: 86%

Building an efficient intrusion detection system based on feature selection and ensemble classifier

et al. 2020

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“…Han et al have used Wasserstein Generative Adversarial Network (WGAN) [56] to process data for traffic, nevertheless, models based on Generative Adversarial Network (GAN) requires a very long training time and the model is very hard to train as well. Many scholars used GBDT to deal with network traffic [37]- [41], and get a great result. Hao et al [34] and Xu et al [35] used GRU to utilize the temporal relationship of network flow, however, GRU is a version of RNN model which focuses on the inner long-term temporal relationship in dataflow but have a relatively poor ability to catch spatial information.…”

Section: Related Work a Intrusion Detection Techniquesmentioning

confidence: 99%

“…In recent years, with the prosperity and development of machine learning techniques, more and more researcher try to use machine learning method to deal with IDS problem [13]- [16]. Lots of scholars have used Support Vector Machine (SVM) [17]- [19], Convolutional Neural Network (CNN) [20]- [23], Recurrent Neural Network (RNN) [24], [25], Long Short-Term Memory (LSTM) [26]- [30],Gated Recurrent Unit (GRU) [34]- [36], ensemble learning method [31]- [33],especially Gradient Boosting Decision Tree (GBDT) [37]- [41] and many other kinds of machine learning meth-ods in IDS. Those methods have efficiently improved the classification accuracy.…”

Section: Introductionmentioning

confidence: 99%

A GBDT-Paralleled Quadratic Ensemble Learning for Intrusion Detection System

2020

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The development of computer and network technology has provided convenience to our daily life, however, attack and intrusion in network emerge endlessly. Intrusion Detection System (IDS) has been developed to confront network attacks. As a result, the research of IDS is one of the most popular fields in recent years. This paper proposes a Gradient Boosting Decision Tree (GBDT)-paralleled quadratic ensemble learning method for intrusion detection system. We use GBDT to deal with the spatial part of traffic data and use Gated Recurrent Unit (GRU) model with special modification for network traffic to deal with temporal data. Then, in order to combine the spatial feature and temporal feature, we fuse GBDT model and GRU model to make a quadratic ensemble model as our final intrusion detection system. The experimental results based on CICIDS2017 dataset show that the advanced spatial-temporal intrusion detection system based on ensemble learning achieves better accuracy, recall, precision and F1 score than the state-of-the-art methods. The accuracies of detecting benign, port scan, Distributed Denial of Service (DDoS), infiltration and web attack traffic are up to 99.9%, 99.9%, 99.9%, 99.9%, and 99.9%, respectively. We also use our method in Information-Centric Networking (ICN) dataset and the results show our method achieves much better performance compared with existing methods.

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“…They adopted both training/testing model and 10-fold cross-validation model for regression and classification. Bansal et al [11] observed the performance of XGBoost on intrusion detection system. They compared the efficiency of XGBoost with AdaBoost, Naïve Bayes, multilayer perceptron (MLP), and K-nearest neighbor classification methods.…”

Section: Related Workmentioning

confidence: 99%

XGBoost: 2D-Object Recognition Using Shape Descriptors and Extreme Gradient Boosting Classifier

Monika

Kumar

2020

Advances in Intelligent Systems and Computing

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In this chapter, the performance of eXtreme Gradient Boosting Classifier (XGBClassifier) is compared with other classifiers for 2D object recognition. A fusion of several feature detector and descriptors (SIFT, SURF, ORB, and Shi Tomasi corner detector algorithm) is taken into consideration to achieve the better object recognition results. Various classifiers are experimented with these feature descriptors separately and various combinations of these feature descriptors. The authors have presented the experimental results of public datasets, namely Caltech-101 which is a very challenging image dataset. Various performance measures, i.e., accuracy, precision, recall, F1-score, false positive rate, area under curve, and root mean square error, are evaluated on this multiclass Caltech-101 dataset. A comparison among four modern well-known classifiers, namely Gaussian Naïve Bayes, decision tree, random forest, and XGBClassifier, is made in terms of performance evaluation measures. The chapter demonstrates that XGBClassifier outperforms rather than other classifiers as it achieves high accuracy (88.36%), precision (88.24%), recall (88.36%), F1-score (87.94%), and area under curve (94.07%) when experimented with the fusion of various feature detectors and descriptors (SIFT, SURF, ORB, and Shi Tomasi corner detector).

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Extreme Gradient Boosting Based Tuning for Classification in Intrusion Detection Systems

Cited by 57 publications

References 10 publications

Building an efficient intrusion detection system based on feature selection and ensemble classifier

Building an efficient intrusion detection system based on feature selection and ensemble classifier

A GBDT-Paralleled Quadratic Ensemble Learning for Intrusion Detection System

XGBoost: 2D-Object Recognition Using Shape Descriptors and Extreme Gradient Boosting Classifier

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