Improving Performance of Autoencoder-Based Network Anomaly Detection on NSL-KDD Dataset

Simple item page
dc.citation.volume9
dc.contributor.authorXu W
dc.contributor.authorJang-Jaccard J
dc.contributor.authorSingh A
dc.contributor.authorWei Y
dc.contributor.authorSabrina F
dc.contributor.editorJi Z
dc.date.accessioned2023-11-14T22:04:32Z
dc.date.accessioned2023-11-20T01:38:27Z
dc.date.available2021-09-29
dc.date.available2023-11-14T22:04:32Z
dc.date.available2023-11-20T01:38:27Z
dc.date.issued2021-09-29
dc.description.abstractNetwork anomaly detection plays a crucial role as it provides an effective mechanism to block or stop cyberattacks. With the recent advancement of Artificial Intelligence (AI), there has been a number of Autoencoder (AE) based deep learning approaches for network anomaly detection to improve our posture towards network security. The performance of existing state-of-the-art AE models used for network anomaly detection varies without offering a holistic approach to understand the critical impacts of the core set of important performance indicators of AE models and the detection accuracy. In this study, we propose a novel 5-layer autoencoder (AE)-based model better suited for network anomaly detection tasks. Our proposal is based on the results we obtained through an extensive and rigorous investigation of several performance indicators involved in an AE model. In our proposed model, we use a new data pre-processing methodology that transforms and removes the most affected outliers from the input samples to reduce model bias caused by data imbalance across different data types in the feature set. Our proposed model utilizes the most effective reconstruction error function which plays an essential role for the model to decide whether a network traffic sample is normal or anomalous. These sets of innovative approaches and the optimal model architecture allow our model to be better equipped for feature learning and dimension reduction thus producing better detection accuracy as well as f1-score. We evaluated our proposed model on the NSL-KDD dataset which outperformed other similar methods by achieving the highest accuracy and f1-score at 90.61% and 92.26% respectively in detection.
dc.description.confidentialfalse
dc.format.pagination140136-140146
dc.identifier.author-urlhttp://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000709061200001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=c5bb3b2499afac691c2e3c1a83ef6fef
dc.identifier.citationXu W, Jang-Jaccard J, Singh A, Wei Y, Sabrina F. (2021). Improving Performance of Autoencoder-Based Network Anomaly Detection on NSL-KDD Dataset. IEEE Access. 9. (pp. 140136-140146).
dc.identifier.doi10.1109/ACCESS.2021.3116612
dc.identifier.eissn2169-3536
dc.identifier.elements-typejournal-article
dc.identifier.issn2169-3536
dc.identifier.urihttps://mro.massey.ac.nz/handle/10179/69188
dc.languageEnglish
dc.publisherIEEE
dc.publisher.urihttps://ieeexplore.ieee.org/document/9552882
dc.relation.isPartOfIEEE Access
dc.rightsCC BY 4.0
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectAnomaly detection
dc.subjectData models
dc.subjectTraining
dc.subjectNetwork security
dc.subjectMathematical models
dc.subjectEncoding
dc.subjectTask analysis
dc.subjectNetwork security
dc.subjectintrusion detection systems
dc.subjectnetwork-based IDSs
dc.subjectanomaly detection
dc.subjectNSL-KDD
dc.subjectartificial intelligence
dc.subjectmachine learning
dc.subjectdeep learning
dc.subjectautoencoders
dc.subjectunsupervised learning
dc.titleImproving Performance of Autoencoder-Based Network Anomaly Detection on NSL-KDD Dataset
dc.typeJournal article
pubs.elements-id449261
pubs.organisational-groupOther
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