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Enhanced Botnet Detection Using a Modified Naïve Bayes Algorithm with Laplace Smoothing and FAMD-Based Feature Agglomeration
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DOI：10.21123/2411-7986.5252.png)
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Abstract
En 中文
Botnets turned into a security problem that would put user privacy and security at risk. Progressive and flexible Machine Learning (ML) techniques are necessary for robust botnet revealing. In this study researchers presented an integration of the modified Na & iuml;ve Bayes and M3L algorithms that uses Factor Analysis of Mixed Data (FAMD) for feature aggregation, and Laplace smoothing for adjustment to address the limitation of conventional Na & iuml;ve Bayes classifiers for detecting botnet. The modified algorithm uses Laplace smoothing to address problems with zero-frequency data and improve the classifier's adaptability. FAMD was used to merge continuous and categorical features to improve the algorithm's capacity and handle mixed data types and lowering the feature dimensionality. Better classification performance and less computing complexity follow from this. Through using a dataset of network traffic that includes both benign and botnet, the proposed approach was evaluated. The suggested algorithm achieves a 97.45% accuracy and a Mean Squared Error (MSE) of 0.039. These results show the ability of the combined method in accurately detect botnet activity and reduce related security threats.
Keywords:
Botnet
Factor Analysis of Mixed Data (FAMD)
Na & iuml
ve Bayes (NB)
M3L
Internet of Things (IoT)
Machine Learning (ML)
Journal
B
IF:
1.7
Papers:
120
Citations:
1.9K
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