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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
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    References listed on IDEAS

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    1. 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.
    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. 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.
    4. 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.
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