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      An intrusion detection model to detect zero-day attacks in unseen data using machine learning

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          Abstract

          In an era marked by pervasive digital connectivity, cybersecurity concerns have escalated. The rapid evolution of technology has led to a spectrum of cyber threats, including sophisticated zero-day attacks. This research addresses the challenge of existing intrusion detection systems in identifying zero-day attacks using the CIC-MalMem-2022 dataset and autoencoders for anomaly detection. The trained autoencoder is integrated with XGBoost and Random Forest, resulting in the models XGBoost-AE and Random Forest-AE. The study demonstrates that incorporating an anomaly detector into traditional models significantly enhances performance. The Random Forest-AE model achieved 100% accuracy, precision, recall, F1 score, and Matthews Correlation Coefficient (MCC), outperforming the methods proposed by Balasubramanian et al., Khan, Mezina et al., Smith et al., and Dener et al. When tested on unseen data, the Random Forest-AE model achieved an accuracy of 99.9892%, precision of 100%, recall of 99.9803%, F1 score of 99.9901%, and MCC of 99.8313%. This research highlights the effectiveness of the proposed model in maintaining high accuracy even with previously unseen data.

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            Artificial Intelligence based Network Intrusion Detection with Hyper-Parameter Optimization Tuning on the Realistic Cyber Dataset CSE-CIC-IDS2018 using Cloud Computing.

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              Malware Detection Using Memory Analysis Data in Big Data Environment

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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: InvestigationRole: Project administrationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: InvestigationRole: Project administrationRole: SupervisionRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Funding acquisitionRole: SupervisionRole: Writing – review & editing
                Role: Data curationRole: Formal analysisRole: Investigation
                Role: Funding acquisitionRole: ValidationRole: Writing – review & editing
                Role: Formal analysisRole: Investigation
                Role: Data curationRole: InvestigationRole: Project administrationRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS One
                plos
                PLOS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                11 September 2024
                2024
                : 19
                : 9
                : e0308469
                Affiliations
                [1 ] Department of Computer System and Technology, Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur, Malaysia
                [2 ] Department of Computer Science and Information Engineering, National Taipei University of Technology, Taipei Taiwan
                [3 ] Department of Computer Science, Universiti Tunku Abdul Rahman, Kampar, Malaysia
                [4 ] Institute for Intelligent Systems Research and Innovation (IISRI) Deakin University, Waurn Ponds, Australia
                [5 ] Department of Computer Science, Faculty of Computer Science and Telecommunications, Cracow University of Technology, Warszawska, Krakow, Poland
                [6 ] Institute of Theoretical and Applied Informatics, Polish Academy of Sciences, Bałtycka, Gliwice, Poland
                Universiti Malaysia Sabah, MALAYSIA
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Author information
                https://orcid.org/0000-0001-7912-1275
                https://orcid.org/0000-0001-5865-1533
                https://orcid.org/0000-0001-7717-9393
                https://orcid.org/0000-0003-0793-3308
                https://orcid.org/0000-0002-4317-2801
                Article
                PONE-D-24-11784
                10.1371/journal.pone.0308469
                11389943
                39259729
                df850bac-e174-4599-a720-bed495f61096
                © 2024 Dai et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 23 March 2024
                : 22 July 2024
                Page count
                Figures: 15, Tables: 6, Pages: 25
                Funding
                Funded by: National Science and Technology Council in Taiwan
                Award ID: NSTC-112-2221-E-027-088-MY2 and NSTC-111-2622-8-027-009
                Award Recipient :
                Funded by: Ministry of Education of Taiwan
                Award ID: 1122302319
                Award Recipient :
                Funded by: UTAR Financial Support for Journal Paper Publication Scheme through Universiti Tunku Abdul Rahman, Malaysia
                Award Recipient :
                This work was supported by the National Science and Technology Council in Taiwan under grant numbers NSTC-112-2221-E-027-088-MY2 and NSTC-111-2622-8-027-009 and also supported by the Ministry of Education of Taiwan under Official Document No. 1122302319 entitled "The study of artificial intelligence and advanced semiconductor manufacturing for female STEM talent education and industry-university value-added cooperation promotion.” and the UTAR Financial Support for Journal Paper Publication Scheme through Universiti Tunku Abdul Rahman (UTAR), Malaysia. The funders had no role in study design, data collection and analysis, the decision to publish, or the preparation of the manuscript.
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                Custom metadata
                The publicly available dataset is analyzed in this study. The data can be found here: CIC-MalMem-2022: https://www.unb.ca/cic/datasets/malmem-2022.html (accessed on 23th March, 2024).

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