A Hybrid Support Vector Machine and Artificial Neural Network Based Cyber Security Framework
| International Journal of Electronics and Communication Engineering |
| © 2024 by SSRG - IJECE Journal |
| Volume 11 Issue 1 |
| Year of Publication : 2024 |
| Authors : Mohammed Ahmed, G. Rama Mohan Babu |
10.14445/23488549/IJECE-V11I1P111
How to Cite?
Mohammed Ahmed, G. Rama Mohan Babu, "A Hybrid Support Vector Machine and Artificial Neural Network Based Cyber Security Framework," SSRG International Journal of Electronics and Communication Engineering, vol. 11, no. 1, pp. 143-149, 2024. Crossref, https://doi.org/10.14445/23488549/IJECE-V11I1P111
Abstract:
Cyber security issues are frequently present in big organizations and government sectors. Cyber security threats would attempt to steal sensitive information from the organization. Thus, it is necessary to avoid the cyber security risks happening on the network to provide users with a secure environment. This was done in our previous research method by introducing the method, namely Big Data Cyber Security Framework (BDCSF). The classification technique used in this work tends to have more computational overhead and might reduce its accuracy with the presence of a large volume of data. These issues are focused on and avoided by proposing a Hybrid Support Vector Machine and Artificial Neural Network Threat Detection Method (HSVMANN-TDM). In this research work, feature selection is initially made at two levels. In the first level, clustering-based feature selection is made using the Improved K means algorithm. The second level cluster of features is given as input to the improved cat swarm algorithm to extract the final selected features. After feature selection, a hybrid SVM ANN algorithm is introduced for threat detection. Here, the output layer of the ANN algorithm will be given as input to the SVM algorithm. This research can predict the fraudulent transactions happening on the network more accurately. The numerical assessment of this research work proves that the proposed research method can perform better than previous works.
Keywords:
Cyber security framework, Optimal feature selection, Clustering-based feature selection, Classification framework, Credit card fraudulent detection.
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