IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v13y2021i18p10057-d631416.html
   My bibliography  Save this article

An Ensemble of Prediction and Learning Mechanism for Improving Accuracy of Anomaly Detection in Network Intrusion Environments

Author

Listed:
  • Imran

    (Computer Engineering Department, Jeju National University, Jeju-si 63243, Korea)

  • Faisal Jamil

    (Computer Engineering Department, Jeju National University, Jeju-si 63243, Korea)

  • Dohyeun Kim

    (Computer Engineering Department, Jeju National University, Jeju-si 63243, Korea)

Abstract

The connectivity of our surrounding objects to the internet plays a tremendous role in our daily lives. Many network applications have been developed in every domain of life, including business, healthcare, smart homes, and smart cities, to name a few. As these network applications provide a wide range of services for large user groups, the network intruders are prone to developing intrusion skills for attack and malicious compliance. Therefore, safeguarding network applications and things connected to the internet has always been a point of interest for researchers. Many studies propose solutions for intrusion detection systems and intrusion prevention systems. Network communities have produced benchmark datasets available for researchers to improve the accuracy of intrusion detection systems. The scientific community has presented data mining and machine learning-based mechanisms to detect intrusion with high classification accuracy. This paper presents an intrusion detection system based on the ensemble of prediction and learning mechanisms to improve anomaly detection accuracy in a network intrusion environment. The learning mechanism is based on automated machine learning, and the prediction model is based on the Kalman filter. Performance analysis of the proposed intrusion detection system is evaluated using publicly available intrusion datasets UNSW-NB15 and CICIDS2017. The proposed model-based intrusion detection accuracy for the UNSW-NB15 dataset is 98.801 percent, and the CICIDS2017 dataset is 97.02 percent. The performance comparison results show that the proposed ensemble model-based intrusion detection significantly improves the intrusion detection accuracy.

Suggested Citation

  • Imran & Faisal Jamil & Dohyeun Kim, 2021. "An Ensemble of Prediction and Learning Mechanism for Improving Accuracy of Anomaly Detection in Network Intrusion Environments," Sustainability, MDPI, vol. 13(18), pages 1-22, September.
  • Handle: RePEc:gam:jsusta:v:13:y:2021:i:18:p:10057-:d:631416
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/13/18/10057/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/13/18/10057/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Imran & Shabir Ahmad & DoHyeun Kim, 2019. "Design and Implementation of Thermal Comfort System based on Tasks Allocation Mechanism in Smart Homes," Sustainability, MDPI, vol. 11(20), pages 1-24, October.
    2. Anam-Nawaz Khan & Naeem Iqbal & Atif Rizwan & Rashid Ahmad & Do-Hyeun Kim, 2021. "An Ensemble Energy Consumption Forecasting Model Based on Spatial-Temporal Clustering Analysis in Residential Buildings," Energies, MDPI, vol. 14(11), pages 1-25, May.
    3. Fazli Wahid & Muhammad Fayaz & Ayman Aljarbouh & Masood Mir & Muhammad Aamir & Imran, 2020. "Energy Consumption Optimization and User Comfort Maximization in Smart Buildings Using a Hybrid of the Firefly and Genetic Algorithms," Energies, MDPI, vol. 13(17), pages 1-26, August.
    4. Imran & Naeem Iqbal & Shabir Ahmad & Do Hyeun Kim, 2021. "Towards Mountain Fire Safety Using Fire Spread Predictive Analytics and Mountain Fire Containment in IoT Environment," Sustainability, MDPI, vol. 13(5), pages 1-23, February.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Anam-Nawaz Khan & Naeem Iqbal & Atif Rizwan & Rashid Ahmad & Do-Hyeun Kim, 2021. "An Ensemble Energy Consumption Forecasting Model Based on Spatial-Temporal Clustering Analysis in Residential Buildings," Energies, MDPI, vol. 14(11), pages 1-25, May.
    2. Martín Pensado-Mariño & Lara Febrero-Garrido & Pablo Eguía-Oller & Enrique Granada-Álvarez, 2021. "Feasibility of Different Weather Data Sources Applied to Building Indoor Temperature Estimation Using LSTM Neural Networks," Sustainability, MDPI, vol. 13(24), pages 1-15, December.
    3. Shahid Nawaz Khan & Syed Ali Abbas Kazmi & Abdullah Altamimi & Zafar A. Khan & Mohammed A. Alghassab, 2022. "Smart Distribution Mechanisms—Part I: From the Perspectives of Planning," Sustainability, MDPI, vol. 14(23), pages 1-109, December.
    4. Anna Borkovcová & Miloslava Černá & Marcela Sokolová, 2022. "Blockchain in the Energy Sector—Systematic Review," Sustainability, MDPI, vol. 14(22), pages 1-12, November.
    5. Imran & Naeem Iqbal & Shabir Ahmad & Do Hyeun Kim, 2021. "Towards Mountain Fire Safety Using Fire Spread Predictive Analytics and Mountain Fire Containment in IoT Environment," Sustainability, MDPI, vol. 13(5), pages 1-23, February.
    6. Bartosz Radomski & Tomasz Mróz, 2021. "The Methodology for Designing Residential Buildings with a Positive Energy Balance—Case Study," Energies, MDPI, vol. 14(16), pages 1-19, August.
    7. Eric Hitimana & Gaurav Bajpai & Richard Musabe & Louis Sibomana & Jayavel Kayalvizhi, 2021. "Implementation of IoT Framework with Data Analysis Using Deep Learning Methods for Occupancy Prediction in a Building," Future Internet, MDPI, vol. 13(3), pages 1-19, March.
    8. Kuldoshbay Avazov & An Eui Hyun & Alabdulwahab Abrar Sami S & Azizbek Khaitov & Akmalbek Bobomirzaevich Abdusalomov & Young Im Cho, 2023. "Forest Fire Detection and Notification Method Based on AI and IoT Approaches," Future Internet, MDPI, vol. 15(2), pages 1-13, January.
    9. Aswin Balasubramanian & Floran Martin & Md Masum Billah & Osaruyi Osemwinyen & Anouar Belahcen, 2021. "Application of Surrogate Optimization Routine with Clustering Technique for Optimal Design of an Induction Motor," Energies, MDPI, vol. 14(16), pages 1-19, August.
    10. Amit Sundas & Sumit Badotra & Salil Bharany & Ahmad Almogren & Elsayed M. Tag-ElDin & Ateeq Ur Rehman, 2022. "HealthGuard: An Intelligent Healthcare System Security Framework Based on Machine Learning," Sustainability, MDPI, vol. 14(19), pages 1-16, September.
    11. Roman V. Klyuev & Irbek D. Morgoev & Angelika D. Morgoeva & Oksana A. Gavrina & Nikita V. Martyushev & Egor A. Efremenkov & Qi Mengxu, 2022. "Methods of Forecasting Electric Energy Consumption: A Literature Review," Energies, MDPI, vol. 15(23), pages 1-33, November.
    12. Baxter Williams & Daniel Bishop & Patricio Gallardo & J. Geoffrey Chase, 2023. "Demand Side Management in Industrial, Commercial, and Residential Sectors: A Review of Constraints and Considerations," Energies, MDPI, vol. 16(13), pages 1-28, July.
    13. Anastasios I. Dounis, 2022. "Machine Intelligence in Smart Buildings," Energies, MDPI, vol. 16(1), pages 1-5, December.
    14. Mateusz Tomal, 2020. "Moving towards a Smarter Housing Market: The Example of Poland," Sustainability, MDPI, vol. 12(2), pages 1-25, January.
    15. Razak Olu-Ajayi & Hafiz Alaka & Hakeem Owolabi & Lukman Akanbi & Sikiru Ganiyu, 2023. "Data-Driven Tools for Building Energy Consumption Prediction: A Review," Energies, MDPI, vol. 16(6), pages 1-20, March.
    16. Jaroslaw Krzywanski, 2022. "Advanced AI Applications in Energy and Environmental Engineering Systems," Energies, MDPI, vol. 15(15), pages 1-3, August.
    17. Tingting Hou & Rengcun Fang & Jinrui Tang & Ganheng Ge & Dongjun Yang & Jianchao Liu & Wei Zhang, 2021. "A Novel Short-Term Residential Electric Load Forecasting Method Based on Adaptive Load Aggregation and Deep Learning Algorithms," Energies, MDPI, vol. 14(22), pages 1-21, November.
    18. Karol Bot & Samira Santos & Inoussa Laouali & Antonio Ruano & Maria da Graça Ruano, 2021. "Design of Ensemble Forecasting Models for Home Energy Management Systems," Energies, MDPI, vol. 14(22), pages 1-37, November.
    19. Daniela Durand & Jose Aguilar & Maria D. R-Moreno, 2022. "An Analysis of the Energy Consumption Forecasting Problem in Smart Buildings Using LSTM," Sustainability, MDPI, vol. 14(20), pages 1-22, October.
    20. Fatma Yaprakdal, 2022. "An Ensemble Deep-Learning-Based Model for Hour-Ahead Load Forecasting with a Feature Selection Approach: A Comparative Study with State-of-the-Art Methods," Energies, MDPI, vol. 16(1), pages 1-13, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:13:y:2021:i:18:p:10057-:d:631416. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.