A Novel Intrusion Detection Approach Using Machine Learning Ensemble for IoT Environments
Source abstract
Abstract
The Internet of Things (IoT) has gained significant importance due to its applicability in diverse environments. Another reason for the influence of the IoT is its use of a flexible and scalable framework. The extensive and diversified use of the IoT in the past few years has attracted cyber-criminals. They exploit the vulnerabilities of the open-source IoT framework due to the absentia of robust and standard security protocols, hence discouraging existing and potential stakeholders. The authors propose a binary classifier approach developed from a machine learning ensemble method to filter and dump malicious traffic to prevent malicious actors from accessing the IoT network and its peripherals. The gradient boosting machine (GBM) ensemble approach is used to train the binary classifier using pre-processed recorded data packets to detect the anomaly and prevent the IoT networks from zero-day attacks. The positive class performance metrics of the model resulted in an accuracy of 98.27%, a precision of 96.40%, and a recall of 95.70%. The simulation results prove the effectiveness of the proposed model against cyber threats, thus making it suitable for critical applications for the IoT.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Network Security and Intrusion Detection · Advanced Malware Detection Techniques · Anomaly Detection Techniques and Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Chitkara University Institute of Engineering and Technology, Chitkara University Punjab, Rajpura 140401, India
Women Institute of Technology, Dehradun 248007, India
School of Electronics and Electrical Engineering, Lovely Professional University, Phagwara 144411, India
Computer Science & Engineering, College of Technology, G.B. Pant University of Agriculture and Technology, Pantnagar 263145, India
School of Computer Science, Mohammed VI Polytechnic University, Ben Guerir 43150, Morocco
References (40)
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