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Anomaly-Based Intrusion Detection Using Machine LearningInternational Journal of Information Security and Privacy10.4018/IJISP.311466

An Efficient Mixed Attribute Outlier Detection Method for Identifying Network Intrusions

J. Rene Beulah, D. Shalini Punithavathani

Source Title: International Journal of Information Security and Privacy (IJISP)14(3)

ISSN: 1930-1650|EISSN: 1930-1669|EISBN13: 9781799805373|DOI: 10.4018/IJISP.2020070107

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MLA

Beulah, J. Rene, and D. Shalini Punithavathani. "An Efficient Mixed Attribute Outlier Detection Method for Identifying Network Intrusions." IJISP vol.14, no.3 2020: pp.115-133. http://doi.org/10.4018/IJISP.2020070107

APA

Beulah, J. R. & Punithavathani, D. S. (2020). An Efficient Mixed Attribute Outlier Detection Method for Identifying Network Intrusions. International Journal of Information Security and Privacy (IJISP), 14(3), 115-133. http://doi.org/10.4018/IJISP.2020070107

Chicago

Beulah, J. Rene, and D. Shalini Punithavathani. "An Efficient Mixed Attribute Outlier Detection Method for Identifying Network Intrusions," International Journal of Information Security and Privacy (IJISP) 14, no.3: 115-133. http://doi.org/10.4018/IJISP.2020070107

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Abstract

Intrusion detection systems (IDS) play a vital role in protecting information systems from intruders. Anomaly-based IDS has established its effectiveness in identifying new and unseen attacks. It learns the normal usage pattern of a network and any event that significantly deviates from the normal behavior is signaled as an intrusion. The crucial challenge in anomaly-based IDS is to reduce false alarm rate. In this article, a clustering-based outlier detection (CBOD) approach is proposed for classifying normal and intrusive patterns. The proposed scheme operates in three modules: an improved hybrid feature selection phase that extracts the most relevant features, a training phase that learns the normal pattern in the training data by forming clusters, and a testing phase that identifies outliers in the testing data. The proposed method is applied for NSL-KDD benchmark dataset and the experimental results yielded a 97.84% detection rate (DR), a 1.88% false alarm rate (FAR), and a 97.96% classification accuracy (ACC). This proposal appears to be promising in terms of DR, FAR and ACC.

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An Efficient Mixed Attribute Outlier Detection Method for Identifying Network Intrusions

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