XGBoost Classifier for DDoS Attack Detection and Analysis in SDN-Based Cloud

Zhuo, Chen; Fu, Jiang; Cheng, Yijun; Gu, Xin; Liu, Weirong; Peng, Jun

doi:10.1109/bigcomp.2018.00044

Cited by 182 publications

(80 citation statements)

References 11 publications

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“…The traversal selects the optimal segmentation point. When the amount of data is large, the method is time-consuming [57,58]. (1) Using the second-order Taylor expression to approximate the objective function, making it easier to find the optimal solution; (2) It can handle sparse and missing data;…”

Section: Xgboostmentioning

confidence: 99%

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GNSS-R Soil Moisture Retrieval Based on a XGboost Machine Learning Aided Method: Performance and Validation

et al. 2019

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Global navigation satellite system (GNSS)-reflectometry is a type of remote sensing technology and can be applied to soil moisture retrieval. Until now, various GNSS-R soil moisture retrieval methods have been reported. However, there still exist some problems due to the complexity of modeling and retrieval process, as well as the extreme uncertainty of the experimental environment and equipment. To investigate the behavior of bistatic GNSS-R soil moisture retrieval process, two ground-truth measurements with different soil conditions were carried out and the performance of the input variables was analyzed from the mathematical statistical aspect. Moreover, the feature of XGBoost method was utilized as well. As a recently developed ensemble machine learning method, the XGBoost method just emerged for the classification of remote sensing and geographic data, to investigate the characterization of the input variables in the GNSS-R soil moisture retrieval. It showed a good correlation with the statistical analysis of ground-truth measurements. The variable contributions for the input data can also be seen and evaluated. The study of the paper provides some experimental insights into the behavior of the GNSS-R soil moisture retrieval. It is worthwhile before establishing models and can also help with understanding the underlying GNSS-R phenomena and interpreting data.

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Section: Xgboostmentioning

confidence: 99%

“…The traversal selects the optimal segmentation point. When the amount of data is large, the method is time-consuming [57,58].…”

Section: Xgboostmentioning

confidence: 99%

GNSS-R Soil Moisture Retrieval Based on a XGboost Machine Learning Aided Method: Performance and Validation

et al. 2019

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“…As we assume above, the McNN is processing the � input, therefore, the overall energy function is defined as in Eq. (6).…”

Section: ) Projection Based Learning (Pbl) Algorithmmentioning

confidence: 99%

“…Prediction of bioactive molecule facilitates the computer-aided drug discovery and XGBoost has shown a good performance on various datasets used [5]. XGboost has been used for the classification of DDoS attack which has shown significant performance compared to SVM and Random Forest [6]. Diabetes detection on a larger dataset using boosting approach has shown to be easily scalable [7].…”

Section: Introductionmentioning

confidence: 99%

Exploratory Boosted Feature Selection and Neural Network Framework for Depression Classification

Arun¹,

Krishna²,

Arunkumar³

et al. 2018

IJIMAI

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Depression is a burdensome psychiatric disease common in low and middle income countries causing disability, morbidity and mortality in late life. In this study, we demonstrate a novel approach for detection of depression using clinical data obtained from the ongoing Mysore Studies of Natal effects on Ageing and Health (MYNAH), in South India where the members have undergone a comprehensive assessment for cognitive function, mental health and cardiometabolic disorders. The proposed model is developed using machine learning approach for classification of depression using Meta-Cognitive Neural Network (McNN) classifier with Projection-based learning (PBL) to address the self-regulating principles like how, what and when to learn. XGBoost is used for feature selection on the available data of assessments with improved confidence. To improve the efficiency of McNN-PBL classifier the best parameters are found using Particle Swarm Optimization (PSO) algorithm. The results indicate that the McNN-PBL classifier selects appropriate records to learn and remove repetitive records which improve the generalization performance. The study helps the clinician to identify the best parameters to analyze the patient.

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“…And it has been applied in the field of wind turbine fault detection [25]. Literature [25][26][27] show that the XGboost classifier not only has faster prediction speed than the other classifiers such as support vector machine (SVM) and deep belief network (DBN), but it also has higher prediction accuracy.…”

Section: Introductionmentioning

confidence: 99%

Online Evaluation Method for Low Frequency Oscillation Stability in a Power System Based on Improved XGboost

Hu¹,

Ji²,

Jin

et al. 2018

Energies

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Low frequency oscillation in an interconnected power system is becoming an increasingly serious problem. It is of great practical significance to make online evaluation of actual power grid's stability. To evaluate the stability of the power system quickly and accurately, a low frequency oscillation stability evaluation method based on an improved XGboost algorithm and power system random response data is proposed in this paper. Firstly, the original input feature set describing the dynamic characteristics of the power system is established by analyzing the substance of low frequency oscillation. Taking the random response data of power system including the disturbance end time feature and the dynamic feature of power system as the input sample set, the wavelet threshold is applied to improve its effectiveness. Secondly, using the eigenvalue analysis method, different damping ratios are selected as threshold values to judge the stability of the system low-frequency oscillation. Then, the supervised training with improved XGboost algorithm is performed on the characteristics of stability. On this basis, the training model is obtained and applied to online low frequency oscillation stability evaluation of a power system. Finally, the simulation results of the eight-machine 36-node test system and Hebei southern power grid show that the proposed low frequency oscillation online evaluation method has the features of high evaluation accuracy, fast evaluation speed, low error rate of unstable sample evaluation, and strong anti-noise ability.Energies 2018, 11, 3238 2 of 18 don't consider the actual uncertainties, and it is difficult to fully reflect the stability level of low-frequency oscillations in actual systems. Therefore, the probabilistic analysis method is introduced. And the statistical probability index of the small signal stability is established by considering the random variables such as state and force variation, load fluctuation, and line parameter variation under various working conditions [5,6]. In literature [7], a small signal stability frequency estimation method is proposed by introducing the Monte Carlo method to the random variables such as load level and form, generator state, and network topology parameters. However, the probability model of random variables is relatively simple, so that the evaluation results cannot accurately reflect the actual situation of the grid. In literature [8,9], the problem is solved. Complex systems require a large amount of computation and long simulation time, so it is necessary to further study more effective methods for evaluating the low-frequency oscillation stability. Based on the eigenvalue analysis method and risk assessment method, considering the probability safety and instability of the system, the literature [10] proposed a method to quickly evaluate the real-time risk of small-scale power grid, but did not consider the uncertainty of the grid. Literature [11] studies the probability distribution of system vibration modal damping based on the determinist...

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XGBoost Classifier for DDoS Attack Detection and Analysis in SDN-Based Cloud

Cited by 182 publications

References 11 publications

GNSS-R Soil Moisture Retrieval Based on a XGboost Machine Learning Aided Method: Performance and Validation

GNSS-R Soil Moisture Retrieval Based on a XGboost Machine Learning Aided Method: Performance and Validation

Exploratory Boosted Feature Selection and Neural Network Framework for Depression Classification

Online Evaluation Method for Low Frequency Oscillation Stability in a Power System Based on Improved XGboost

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