Comparison of Performance of Classification Algorithms Using Standard Deviation-based Feature Selection in Cyber Attack Datasets

Supervised machine learning techniques are commonly used in many areas like finance, education, healthcare, engineering, etc. because of their ability to learn from past data. However, such can be very slow if the dataset is high-dimensional, and also irrelevant features may reduce classification success. Therefore, feature selection or reduction overcome mentioned issues. On other hand, information security for both people networks crucial, it must secured without wasting time. Hence, approaches that make algorithms faster reducing success needed. In this study, we compare run-time performance state-of-the-art using standard deviation-based aspect datasets. For purpose, applied KDD Cup 99 Phishing Legitimate datasets selecting most relevant features, then run selected on results. According obtained results, while all satisfying Decision Tree (DT) was best one among others. Tree, k Nearest Neighbors, Naïve Bayes (BN) were sufficiently fast, Support Vector Machine (SVM) Artificial Neural Networks (ANN NN) too slow.