Bi-Layer Behavioral-Based Feature Selection Approach for Network Intrusion Classification

Eid, Heba F.; Hassanien, Aboul Ella; Kim, Tai-hoon

doi:10.1007/978-3-642-27189-2_21

Cited by 21 publications

(13 citation statements)

References 15 publications

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“…The authors improved their work by using information gain to rank the features and then a behaviorbased feature selection to reduce the feature set to 20. This resulted in an improvement in reported accuracy using the training dataset [12].…”

Section: Related Workmentioning

confidence: 99%

A Deep Learning Approach for Network Intrusion Detection System

Javaid¹,

Niyaz²,

Sun³

et al. 2016

Proceedings of the 9th EAI International Conference on Bio-Inspired Information and Communications Technologies (Formerly BIONE

689

344

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A Network Intrusion Detection System (NIDS) helps system administrators to detect network security breaches in their organizations. However, many challenges arise while developing a flexible and efficient NIDS for unforeseen and unpredictable attacks. We propose a deep learning based approach for developing such an efficient and flexible NIDS. We use Self-taught Learning (STL), a deep learning based technique, on NSL-KDD -a benchmark dataset for network intrusion. We present the performance of our approach and compare it with a few previous work. Compared metrics include accuracy, precision, recall, and f-measure values.

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Section: Related Workmentioning

confidence: 99%

A Deep Learning Approach for Network Intrusion Detection System

Javaid¹,

Niyaz²,

Sun³

et al. 2016

Proceedings of the 9th EAI International Conference on Bio-Inspired Information and Communications Technologies (Formerly BIONE

689

344

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“…All of the aforementioned detection techniques were evaluated on the KDD Cup 99 dataset. However, due to some limitations in this dataset, which will be discussed in Subsection 5.1, some other detection methods [18], [19], [20], [21], [22], [23] were evaluated using other intrusion detection datasets, such as NSL-KDD [24] and Kyoto 2006+ [25]. A dimensionality reduction method proposed in [25] was to find the most important features involved in building a naive bayesian classifier for intrusion detection.…”

Section: Performance Evaluationmentioning

confidence: 99%

Building an Intrusion Detection System Using a Filter-Based Feature Selection Algorithm

Ambusaidi

Nanda

et al. 2016

IEEE Trans. Comput.

497

242

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Redundant and irrelevant features in data have caused a long-term problem in network traffic classification. These features not only slow down the process of classification but also prevent a classifier from making accurate decisions, especially when coping with big data. In this paper, we propose a mutual information based algorithm that analytically selects the optimal feature for classification. This mutual information based feature selection algorithm can handle linearly and nonlinearly dependent data features. Its effectiveness is evaluated in the cases of network intrusion detection. An Intrusion Detection System (IDS), named Least Square Support Vector Machine based IDS (LSSVM-IDS), is built using the features selected by our proposed feature selection algorithm. The performance of LSSVM-IDS is evaluated using three intrusion detection evaluation datasets, namely KDD Cup 99, NSL-KDD and Kyoto 2006+ dataset. The evaluation results show that our feature selection algorithm contributes more critical features for LSSVM-IDS to achieve better accuracy and lower computational cost compared with the state-of-the-art methods.

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“…La selección de atributos para mejorar el rendimiento de los métodos empleados para la detección de anomalías es una tarea ardua que ha sido llevada a cabo de diferentes maneras en el estado-del-arte. En [15] se propone un método para seleccionar rasgos basado en dos capas. La primera capa emplea Ganancia de Información para ordenar los atributos y generar un nuevo conjunto de atributos basándose en una medida global de precisión.…”

Section: Materiales Y Métodosunclassified

“…Los métodos de detección de anomalías basadas en aprendizaje automatizado han devenido en una herramienta útil debido a la capacidad de tratar con grandes cantidades de datos y a la capacidad de detectar ataques nunca antes vistos. De manera general en este campo de investigación se emplean métodos basados en naives Bayes [7], [8], [9], máquinas de vector soporte [10], [11], [12], basados en vecindad [13], [14], árboles de decisión [15], [16], esquemas de múltiples clasificadores [17], [18] y redes neuronales y aprendizaje profundo [19], [20], [21], [22], [23], entre otros [24], [25].…”

Section: Introductionunclassified

Esquemas de combinación de árboles de decisión como estrategia para la detección de anomalías

Quintana-Zaez¹,

Velarde-Bedregal²,

Anton-Vargas³

et al. 2020

Proceedings of the 18th LACCEI International Multi-Conference for Engineering, Education, and Technology: Engineering, Integrat

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Security of the data consumed, generated and stored is crucial to the quality of life in today's society. For this reason, this paper proposes a comparative study of different combination schemes of multiple classifiers based on decision trees, due to its scalability and easy implementation. As a result, precision and recall values of about 97% and 100% were obtained, showing their high reliability, reducing false alarms and high generalization capacity. A comparison with a deep learning based algorithm showed that tree combination strategies are competitive and with statistically similar and superior results to the state-of-the-art. In the end, the results suggest that adaptive strategies such as XGBoost or highly randomized strategies such as Random Forest or Extra-Tree can be alternatives for the protection of precious data on the network.

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Bi-Layer Behavioral-Based Feature Selection Approach for Network Intrusion Classification

Cited by 21 publications

References 15 publications

A Deep Learning Approach for Network Intrusion Detection System

A Deep Learning Approach for Network Intrusion Detection System

Building an Intrusion Detection System Using a Filter-Based Feature Selection Algorithm

Esquemas de combinación de árboles de decisión como estrategia para la detección de anomalías

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