Abstract
The evolution of computer networks and Internet of Things (IoT) in various fields increases the privacy, and security concerns. The increased usage of network-related applications demands a cost-efficient cyber security framework to protect the system from attackers. In this article, an optimized neural-based cyber security model named Golden Eagle-based Dense Neural System (GEbDNS) was designed to detect the intrusion in the network. Initially, the network dataset CICIDS 2017 was collected and imported into the network. The dataset contains both normal and abnormal data. The raw dataset was pre-processed to eliminate the training flaws and errors and the important data features are extracted. Further, the optimal data features are selected for detection phase using the optimal solution of golden eagle optimization. Then, the selected data features are matched with the trained attack data for attack classification. Finally, the results are evaluated and verified with existing techniques in terms of accuracy, true-positive rate, and false-positive rate. The experimental analysis states that the developed security framework outperforms the traditional schemes with greater accuracy of 98.45%.
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The authors would like to thank the reviewers for all of their careful, constructive and insightful comments in relation to this work.
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Veerasamy, B., Nageswari, D., Kumar, S.N., Shirgire, A., Sitharthan, R., Jasmine Gnana Malar, A. (2023). An Optimized Cyber Security Framework for Network Applications. In: Bhateja, V., Carroll, F., Tavares, J.M.R.S., Sengar, S.S., Peer, P. (eds) Intelligent Data Engineering and Analytics. FICTA 2023. Smart Innovation, Systems and Technologies, vol 371. Springer, Singapore. https://doi.org/10.1007/978-981-99-6706-3_45
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DOI: https://doi.org/10.1007/978-981-99-6706-3_45
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