  Detecting cyber-anomalies and attacks are becoming a rising concern these
days in the domain of cybersecurity. The knowledge of artificial intelligence,
particularly, the machine learning techniques can be used to tackle these
issues. However, the effectiveness of a learning-based security model may vary
depending on the security features and the data characteristics. In this paper,
we present "CyberLearning", a machine learning-based cybersecurity modeling
with correlated-feature selection, and a comprehensive empirical analysis on
the effectiveness of various machine learning based security models. In our
CyberLearning modeling, we take into account a binary classification model for
detecting anomalies, and multi-class classification model for various types of
cyber-attacks. To build the security model, we first employ the popular ten
machine learning classification techniques, such as naive Bayes, Logistic
regression, Stochastic gradient descent, K-nearest neighbors, Support vector
machine, Decision Tree, Random Forest, Adaptive Boosting, eXtreme Gradient
Boosting, as well as Linear discriminant analysis. We then present the
artificial neural network-based security model considering multiple hidden
layers. The effectiveness of these learning-based security models is examined
by conducting a range of experiments utilizing the two most popular security
datasets, UNSW-NB15 and NSL-KDD. Overall, this paper aims to serve as a
reference point for data-driven security modeling through our experimental
analysis and findings in the context of cybersecurity.
