Ensemble classifiers for supervised anomaly based network intrusion detection

Timčenko, Valentina; Gajin, Slavko

doi:10.1109/iccp.2017.8116977

Cited by 28 publications

(18 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These weak classifiers are trained using the training dataset and a combined ensemble prediction model is generated. Ensemble classifiers also allow for updating the data sources of the weak classifiers, thereby eliminating the need for retraining [27]. They offer the advantage of achieving improved prediction results due to the diversity of weak classifier outputs, since each type of data may represent varied characteristics of the instance to be classified [27].…”

Section: Proposed Ensemble Classafier Based Detectormentioning

confidence: 99%

“…Ensemble classifiers also allow for updating the data sources of the weak classifiers, thereby eliminating the need for retraining [27]. They offer the advantage of achieving improved prediction results due to the diversity of weak classifier outputs, since each type of data may represent varied characteristics of the instance to be classified [27]. These classifiers are highly efficient in improving accuracy and reducing false alarms [27].…”

Section: Proposed Ensemble Classafier Based Detectormentioning

confidence: 99%

“…They offer the advantage of achieving improved prediction results due to the diversity of weak classifier outputs, since each type of data may represent varied characteristics of the instance to be classified [27]. These classifiers are highly efficient in improving accuracy and reducing false alarms [27]. Figure 6 presents a summarized concept of the ensemble classifier.…”

Section: Proposed Ensemble Classafier Based Detectormentioning

confidence: 99%

“…The use of ensemble learning methods is another practical way of achieving higher detection accuracy. Recently, ensemble classifiers have been used in many detection problems and have shown promising results [26][27][28][29][30][31][32][33]. Ensemble classifiers make use of multiple learning algorithms in order to achieve a prediction efficiency higher than any of their base learners [34][35][36].…”

Section: Introductionmentioning

confidence: 99%

“…Depression detection in speech using ensemble method is proposed in [26]. In [27], ensemble classifier is used for network intrusion detection. In [29], ensemble decision trees are used for electrocardiograph artifact detection.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Ensemble Classifier Based Spectrum Sensing in Cognitive Radio Networks

Ahmad

2019

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

Spectrum sensing is one of the most important and challenging tasks in cognitive radio. To develop methods of dynamic spectrum access, robust and efficient spectrum sensors are required. For most of these sensors, the main constraints are the lack of information about the primary user's (PU) signal, high computational cost, performance limits in low signal-to-noise ratio (SNR) conditions, and difficulty in finding a detection threshold. This paper proposes a machine learning based novel detection method to overcome these limits. To address the first constraint, detection is achieved using cyclostationary features. The constraints of low SNR, finding detection threshold, and computational cost are addressed by proposing an ensemble classifier. First, a dataset is generated containing different orthogonal frequency-division multiplexing signals at different SNRs. Then, cyclostationary features are extracted using FFT accumulation method. Finally, the proposed ensemble classifier has been trained using the extracted features to detect PU's signal in low SNR conditions. This ensemble classifier is based on decision trees and AdaBoost algorithm. A comparison of the proposed classifier with another machine learning classifier, namely, support vector machine (SVM), is presented, clearly showing that the ensemble classifier outperforms SVM. The results of the simulation also prove the robustness and superior efficiency of the detector proposed in this paper in comparison with a cyclostationary detector without machine learning as well as the classical energy detector.

show abstract

Section: Proposed Ensemble Classafier Based Detectormentioning

confidence: 99%