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Network intrusion detection using oversampling technique and machine learning algorithms

Ahmed, Hafiza Anisa ; Hameed, Anum ; Bawany, Narmeen Zakaria

PeerJ. Computer science, 2022-01, Vol.8, p.e820-e820, Article e820 [Periódico revisado por pares]

United States: PeerJ. Ltd

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Título:
Network intrusion detection using oversampling technique and machine learning algorithms
Autor: Ahmed, Hafiza Anisa ; Hameed, Anum ; Bawany, Narmeen Zakaria
Assuntos: Accuracy ; Algorithms ; Artificial neural networks ; Classification ; Communications traffic ; Computer architecture ; Computer networks ; Computer Networks and Communications ; Data mining ; Data Mining and Machine Learning ; Datasets ; Decision trees ; Detectors ; Feature selection ; Imbalanced classes ; Intrusion detection systems ; Machine learning ; Methods ; Model accuracy ; Network attack ; Network intrusion detection system (NIDS) ; Neural networks ; Oversampling ; Principal components analysis ; Safety and security measures ; Security ; Security and Privacy ; Support vector machines ; Synthetic minority over-sampling technique (SMOTE) ; Traffic flow ; Traffic information ; UNSW-NB15 dataset
É parte de: PeerJ. Computer science, 2022-01, Vol.8, p.e820-e820, Article e820
Notas: ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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Descrição: The expeditious growth of the World Wide Web and the rampant flow of network traffic have resulted in a continuous increase of network security threats. Cyber attackers seek to exploit vulnerabilities in network architecture to steal valuable information or disrupt computer resources. Network Intrusion Detection System (NIDS) is used to effectively detect various attacks, thus providing timely protection to network resources from these attacks. To implement NIDS, a stream of supervised and unsupervised machine learning approaches is applied to detect irregularities in network traffic and to address network security issues. Such NIDSs are trained using various datasets that include attack traces. However, due to the advancement in modern-day attacks, these systems are unable to detect the emerging threats. Therefore, NIDS needs to be trained and developed with a modern comprehensive dataset which contains contemporary common and attack activities. This paper presents a framework in which different machine learning classification schemes are employed to detect various types of network attack categories. Five machine learning algorithms: Random Forest, Decision Tree, Logistic Regression, K-Nearest Neighbors and Artificial Neural Networks, are used for attack detection. This study uses a dataset published by the University of New South Wales (UNSW-NB15), a relatively new dataset that contains a large amount of network traffic data with nine categories of network attacks. The results show that the classification models achieved the highest accuracy of 89.29% by applying the Random Forest algorithm. Further improvement in the accuracy of classification models is observed when Synthetic Minority Oversampling Technique (SMOTE) is applied to address the class imbalance problem. After applying the SMOTE, the Random Forest classifier showed an accuracy of 95.1% with 24 selected features from the Principal Component Analysis method.
Editor: United States: PeerJ. Ltd
Idioma: Inglês

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Network intrusion detection using oversampling technique and machine learning algorithms

Ahmed, Hafiza Anisa ; Hameed, Anum ; Bawany, Narmeen Zakaria

United States: PeerJ. Ltd

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