Journal
APPLIED SCIENCES-BASEL
Volume 11, Issue 21, Pages -Publisher
MDPI
DOI: 10.3390/app112110268
Keywords
machine learning; intrusion detection system; network anomaly detection; Internet of Things; gradient boosting machine (GBM)
Ask authors/readers for more resources
The Internet of Things (IoT) has gained significant importance due to its applicability in diverse environments, but the absence of security protocols has attracted cyber-criminals. The authors propose a binary classifier approach using machine learning to prevent malicious actors from accessing the IoT network and effectively combat network threats.
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available