A novel deep learning based security assessment framework for enhanced security in swarm network environment

Zhiqiang Liu, Mohi-ud-din Ghulam, Jiangbin Zheng, Sifei Wang and Asim Muhammad

International Journal of Critical Infrastructure Protection, 2022, vol. 38, issue C

Abstract: Security assessments are essential in network systems to improve the reliability of the environment. This study presents a deep learning-based security assessment model as a proactive approach for monitoring network activities. This approach can improve security across the network environment and connected computing infrastructures by detecting and classifying various types of security attacks. Deep learning is one of the emerging solutions for integrating intelligent and smart techniques into traditional solutions for improving the performance of security detection. Leveraging a multilayer perceptron (MLP) combined with an XGBoost classifier for large-scale data processing and classification, the performance of the approach demonstrated an accuracy of 93.30% and a precision of 92.73% for malicious attack detection.

Keywords: Security assessment framework; Deep Learning; Multilayer Perceptron (MLP); Network monitoring; Security detection (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ijocip:v:38:y:2022:i:c:s1874548222000294

DOI: 10.1016/j.ijcip.2022.100540

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