The rapid increase in network traffic has recently led to the importance of flow-based intrusion detection systems processing a small amount of traffic data. Furthermore, anomaly-based methods, which can identify unknown attacks are also integrated into these systems. In this study, the focus is concentrated on the detection of anomalous network traffic (or intrusions) from flow-based data using unsupervised deep learning methods with semi-supervised learning approach. More specifically, Autoencoder and Variational Autoencoder methods were employed to identify unknown attacks using flow features. In the experiments carried out, the flow-based features extracted out of network traffic data, including typical and different types of attacks, were used. The Receiver Operating Characteristics (ROC) and the area under ROC curve, resulting from these methods were calculated and compared with One-Class Support Vector Machine. The ROC curves were examined in detail to analyze the performance of the methods in various threshold values. The experimental results show that Variational Autoencoder performs, for the most part, better than Autoencoder and One-Class Support Vector Machine.

The computer science students mostly face with the difficulties in learning the topics of algorithms courses. Only listening the topic from the teacher or just writing makes the learning volatile. Instead of listening or writing, if there is something visual, it would be more permanent to learn because visuality increases the learning potential and the time for learning is minimized. The adversities of classical education techniques were intended to be eliminated in this study via computer games which are becoming more and more popular in this age. An educational convenience is provided for the subject of tree traversal algorithms. Tree traversal algorithms are one of the basic and confused concepts in algorithms and programming courses in computer science. A game called “binary apple tree” was established to teach and learn the subject easier.