Ensemble Feature Selection and Classification of Internet Traffic using XGBoost Classifier

Manju, N.; Harish, B. S.; Prajwal, V

doi:10.5815/ijcnis.2019.07.06

Cited by 23 publications

(14 citation statements)

References 30 publications

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“…The proposed model is evaluated by the detection rate and the false positive rate. We select the state of the art six methods for the comparison of malicious traffic detection, two of which are detection models based on machine learning, and the other four are traffic detection models based on deep learning, special as follows: XGBoost 28 : The model is biased towards classes with more samples, and because the features of the minority class are usually regarded as noise and therefore ignored, they also tend to predict the problem of only the majority class data. An integrated classification model of XGBoost combined with a tree method is proposed to improve classification performance.…”

Section: Resultsmentioning

confidence: 99%

“…XGBoost 28 : The model is biased towards classes with more samples, and because the features of the minority class are usually regarded as noise and therefore ignored, they also tend to predict the problem of only the majority class data. An integrated classification model of XGBoost combined with a tree method is proposed to improve classification performance.…”

Section: Resultsmentioning

confidence: 99%

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Malicious traffic detection combined deep neural network with hierarchical attention mechanism

Liu

2021

Sci Rep

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Given the gradual intensification of the current network security situation, malicious attack traffic is flooding the entire network environment, and the current malicious traffic detection model is insufficient in detection efficiency and detection performance. This paper proposes a data processing method that divides the flow data into data flow segments so that the model can improve the throughput per unit time to meet its detection efficiency. For this kind of data, a malicious traffic detection model with a hierarchical attention mechanism is also proposed and named HAGRU (Hierarchical Attention Gated Recurrent Unit). By fusing the feature information of the three hierarchies, the detection ability of the model is improved. An attention mechanism is introduced to focus on malicious flows in the data flow segment, which can reasonably utilize limited computing resources. Finally, compare the proposed model with the current state of the method on the datasets. The experimental results show that: the novel model performs well in different evaluation indicators (detection rate, false-positive rate, F-score), and it can improve the performance of category recognition with fewer samples when the data is unbalanced. At the same time, the training of the novel model on larger datasets will enhance the generalization ability and reduce the false alarm rate. The proposed model not only improves the performance of malicious traffic detection but also provides a new research method for improving the efficiency of model detection.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Malicious traffic detection combined deep neural network with hierarchical attention mechanism

Liu

2021

Sci Rep

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show abstract

“…The main advantage of XGBoost is its scalability and execution speed, which are far better than other machine learning algorithms [43]. Manju et al [44] noted that models have a bias towards classes with more samples. They also tend to predict only the majority class data as features of the minority class are often treated as noise and therefore ignored.…”

Section: Xgboost Modelmentioning

confidence: 99%

“…As a result, the authors proposed an ensemble classification model using XGboost to enhance the performance of classification. Compared with other models, XGBoost proved better classification accuracy when dealing with classification problems [44]. We used Python libraries to implement all XGBoost processes.…”

Section: Xgboost Modelmentioning

confidence: 99%

The Evaluation of Color Spaces for Large Woody Debris Detection in Rivers Using XGBoost Algorithm

2022

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Large woody debris (LWD) strongly influences river systems, especially in forested and mountainous catchments. In Taiwan, LWD are mainly from typhoons and extreme torrential events. To effectively manage the LWD, it is necessary to conduct regular surveys on river systems. Simple, low cost, and accurate tools are therefore necessary. The proposed methodology applies image processing and machine learning (XGBoost classifier) to quantify LWD distribution, location, and volume in river channels. XGBoost algorithm was selected due to its scalability and faster execution speeds. Nishueibei River, located in Taitung County, was used as the area of investigation. Unmanned aerial vehicles (UAVs) were used to capture the terrain and LWD. Structure from Motion (SfM) was used to build high-resolution orthophotos and digital elevation models (DEM), after which machine learning and different color spaces were used to recognize LWD. Finally, the volume of LWD in the river was estimated. The findings show that RGB color space as LWD recognition factor suffers serious collinearity problems, and it is easy to lose some LWD information; thus, it is not suitable for LWD recognition. On the contrary, the combination of different factors in different color spaces enhances the results, and most of the factors are related to the YCbCr color space. The CbCr factor in the YCbCr color space was best for identifying LWD. LWD volume was then estimated from the identified LWD using manual, field, and automatic measurements. The results indicate that the manual measurement method was the best (R2 = 0.88) to identify field LWD volume. Moreover, automatic measurement (R2 = 0.72) can also obtain LWD volume to save time and workforce.

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“…Saber et al [58] achieved traffic feature selection based on linear discriminant analysis. Manju et al [59] ranked traffic features according to the feature weights which were the number of times that each feature appears in the tree, and selected the optimal feature subset based on the accuracies of the extreme gradient boosting model. Jamil et al [60] created several candidate feature subsets by different feature selection algorithms, and chose the best subset according to the results of all the feature subsets' evaluations based on the five ML algorithms.…”

Section: Related Workmentioning

confidence: 99%

Improved KNN Algorithm for Fine-Grained Classification of Encrypted Network Flow

Cao

2020

Electronics

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The fine-grained classification of encrypted traffic is important for network security analysis. Malicious attacks are usually encrypted and simulated as normal application or content traffic. Supervised machine learning methods are widely used for traffic classification and show good performances. However, they need a large amount of labeled data to train a model, while labeled data is hard to obtain. Aiming at solving this problem, this paper proposes a method to train a model based on the K-nearest neighbor (KNN) algorithm, which only needs a small amount of data. Due to the fact that the importance of different traffic features varies, and traditional KNN does not highlight the importance of different features, this study introduces the concept of feature weight and proposes the weighted feature KNN (WKNN) algorithm. Furthermore, to obtain the optimal feature set and the corresponding feature weight set, a feature selection and feature weight self-adaptive algorithm for WKNN is proposed. In addition, a three-layer classification framework for encrypted network flows is established. Based on the improved KNN and the framework, this study finally presents a method for fine-grained classification of encrypted network flows, which can identify the encryption status, application type and content type of encrypted network flows with high accuracies of 99.3%, 92.4%, and 97.0%, respectively.

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Ensemble Feature Selection and Classification of Internet Traffic using XGBoost Classifier

Cited by 23 publications

References 30 publications

Malicious traffic detection combined deep neural network with hierarchical attention mechanism

Malicious traffic detection combined deep neural network with hierarchical attention mechanism

The Evaluation of Color Spaces for Large Woody Debris Detection in Rivers Using XGBoost Algorithm

Improved KNN Algorithm for Fine-Grained Classification of Encrypted Network Flow

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