Next Article in Journal
Analysis of Reasons for the Structural Collapse of Historic Buildings
Previous Article in Journal
Comparison of Research Data of Diesel–Biodiesel–Isopropanol and Diesel–Rapeseed Oil–Isopropanol Fuel Blends Mixed at Different Proportions on a CI Engine
Article

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

Computer Engineering Department, Jeju National University, Jeju-si 63243, Korea
*
Author to whom correspondence should be addressed.
Academic Editor: Manuel Fernandez-Veiga
Sustainability 2021, 13(18), 10057; https://doi.org/10.3390/su131810057
Received: 15 July 2021 / Revised: 27 August 2021 / Accepted: 1 September 2021 / Published: 8 September 2021
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. View Full-Text
Keywords: intrusion detection; intrusion accuracy; automated machine learning; CICIDS2017; UNSW-NB15 intrusion detection; intrusion accuracy; automated machine learning; CICIDS2017; UNSW-NB15
Show Figures

Figure 1

MDPI and ACS Style

Imran; Jamil, F.; Kim, D. An Ensemble of Prediction and Learning Mechanism for Improving Accuracy of Anomaly Detection in Network Intrusion Environments. Sustainability 2021, 13, 10057. https://doi.org/10.3390/su131810057

AMA Style

Imran, Jamil F, Kim D. An Ensemble of Prediction and Learning Mechanism for Improving Accuracy of Anomaly Detection in Network Intrusion Environments. Sustainability. 2021; 13(18):10057. https://doi.org/10.3390/su131810057

Chicago/Turabian Style

Imran, Faisal Jamil, and Dohyeun Kim. 2021. "An Ensemble of Prediction and Learning Mechanism for Improving Accuracy of Anomaly Detection in Network Intrusion Environments" Sustainability 13, no. 18: 10057. https://doi.org/10.3390/su131810057

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop