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.
Highlights
An expeditious rise in the development of network and communication technologies leads to an immense amount of network data generated from a wide range of services
There is a dire need to tune the contemporary data mining and statistical methods to address the challenges of the growing internet applications, such as bandwidth handling, network intrusion detection, and scalability
This paper presents an intrusion detection system based on the ensemble of prediction and learning mechanisms to improve intrusion detection accuracy
Summary
An expeditious rise in the development of network and communication technologies leads to an immense amount of network data generated from a wide range of services. A wide range of network applications is developed in every domain of life, including business, healthcare, smart homes, and smart cities, to name a few [4,5,6,7]. The plethora of high-dimensional data increases the need for analysis tools based on advanced data mining and statistical methods [8,9]. There is a dire need to tune the contemporary data mining and statistical methods to address the challenges of the growing internet applications, such as bandwidth handling, network intrusion detection, and scalability. The existing state-of-the-art intrusion detection-based systems focus on increasing the reliability aspect of these applications [10]
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