  Cyber attacks constitute a significant threat to organizations with
implications ranging from economic, reputational, and legal consequences. As
cybercriminals' techniques get sophisticated, information security
professionals face a more significant challenge to protecting information
systems. In today's interconnected realm of computer systems, each attack
vector has a network dimension. The present study investigates network
intrusion attempts with anomaly-based machine learning models to provide better
protection than the conventional misuse-based models. Two models, namely an
ensemble learning model and a convolutional neural network model, were built
and implemented on a data set gathered from a real-life, institutional
production environment. To demonstrate the models' reliability and validity,
they were applied to the UNSW-NB15 benchmarking data set. The type of attack
was limited to probing attacks to keep the scope of the study manageable. The
findings revealed high accuracy rates, the CNN model being slightly more
accurate.
