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Hybrid Machine Learning Approach for Attack Classification and Clustering in Network Security
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 185 - Number 31 |
| Year of Publication: 2023 |
| Authors: Castro A. Yoga, Anthony J. Rodrigues, Silvance O. Abeka |
10.5120/ijca2023923076
|
Castro A. Yoga, Anthony J. Rodrigues, Silvance O. Abeka . Hybrid Machine Learning Approach for Attack Classification and Clustering in Network Security. International Journal of Computer Applications. 185, 31 ( Aug 2023), 45-51. DOI=10.5120/ijca2023923076
Abstract
Due to the increasing complexity and diversity of threats, network security has become a critical concern. The application of machine learning (ML) methods has demonstrated potential in enhancing network security by effectively recognizing and classifying threats. A hybrid ML-based approach is presented in this study within the framework of the three-layer network security domain (TLNSD) to address the task of attack classification and clustering. The approach utilizes a stacking ensemble classifier, which employs a meta learner (Logistic Regression) to combine the predictions from multiple base learners (K-Nearest Neighbors, Random Forest, Gaussian Naive Bayes). To identify the most relevant features, the SelectKBest algorithm is employed. Additionally, the K-means clustering technique is utilized to group similar attack instances. The performance evaluation of the proposed technique is conducted using the UNSW-NB15 dataset. The results demonstrate that the proposed technique surpasses the performance of individual base learners, achieving a high level of accuracy. This underscores its effectiveness in detecting and categorizing attacks. The clustering analysis provides insights into the distribution and occurrence frequency of diverse threat types, enabling the development of tailored security strategies. By presenting a comprehensive and integrated approach to threat analysis and mitigation, this study contributes to the advancement of network security. The proposed methodology offers a unified framework to effectively address the challenges posed by evolving cyber threats.
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