Principal Component Analysis (PCA) is a well-known dimensionality reduction technique that has been widely used in various machine learning algorithms. This includes kNN and naivebayesalgorithms which can be time-co...
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ISBN:
(纸本)9783031489808;9783031489815
Principal Component Analysis (PCA) is a well-known dimensionality reduction technique that has been widely used in various machine learning algorithms. This includes kNN and naivebayesalgorithms which can be time-consuming. The reduction of dimensions can have positive effects on those two algorithms by reducing the number of related types of data and decreasing the data they need to analyze. Here we present detailed findings about how the PCA algorithm affects them both in time efficiency and accuracy. All calculations regarding those values were carried out in Python programming language. The dataset used in research is the Titanic dataset, on which data cleaning and normalization were done. The data in this paper suggests that it is possible to maintain the same level of accuracy with great improvement in time efficiency. For the kNN algorithm reducing the number of dimensions by one resulted in a 31.09% increase in accuracy and for the naivebayes algorithm an 18.18% increase while having an imperceptible effect on accuracy.
APT Botnets have started using Information obfuscation techniques include encryption to evade detection. In order to detect encrypted botnet traffic, in this paper we see detection of encrypted botnet traffic from nor...
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ISBN:
(纸本)9781479941711
APT Botnets have started using Information obfuscation techniques include encryption to evade detection. In order to detect encrypted botnet traffic, in this paper we see detection of encrypted botnet traffic from normal network traffic as traffic classification problem. After analyses features of encrypted botnet traffic, we propose a novel meta-level classification algorithm based on content features and flow features of traffic. The content features consist of information entropy and byte frequency distribution, and the flow features consist of port number, payload length and protocol type of application layer. Then we use naive bayes classification algorithms to detect botnet traffic. The related experiment shows that our method has good detection effect.
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