A hybrid machine learning algorithm used to combine two different machine learning algorithms, with the purpose of exploiting their advantages to mend the loopholes in each original design and so to achieve better performance over each individual algorithm. This paper presents a new hybrid machine learning algorithm which combines two existing algorithms - Naive Bayes and C4.5 - to improve both the trained classification model performance and training time in network intrusion detection. As experimental results show, in contrast to other hybrid machine learning algorithms, our proposed algorithm is able to shorten the needed training time and meanwhile yields desirable detection performance.
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Network Intrusion Detection using Hybrid Machine Learning
Semantic Scholar · Computer Science · 2019
Abstract
A hybrid machine learning algorithm used to combine two different machine learning algorithms, with the purpose of exploiting their advantages to mend the loopholes in each original design and so to achieve better performance over each individual algorithm. This paper presents a new hybrid machine learning algorithm which combines two existing algorithms - Naive Bayes and C4.5 - to improve both the trained classification model performance and training time in network intrusion detection. As experimental results show, in contrast to other hybrid machine learning algorithms, our proposed algorithm is able to shorten the needed training time and meanwhile yields desirable detection performance.