The known network attack detection and unknown network attack identification based on deep learning methods

As accessing information has become easier, the encountered threats along the way have become more frequent. When it was realized that these ways of accessing information were not sufficiently reliable, various prevention techniques were implemented. One of the most useful of these measures is the intrusion detection systems. The Intrusion Detection Systems provide a comprehensive analysis of the network. This way, potential threats can be rapidly detected as quickly as possible and the necessary measures can be taken. On the other hand, classifying network traffic is not only important for identifying threats but it is also crucial for gaining insight into the network's overall behaviour. Utilizing generative networks for these purposes has become one of the most popular methods in recent times. In this study, deep learning methods are employed to analyze network traffic by using autoencoder models. In the proposed method, network traffic analysis consists of two stages. The first stage aims to correctly classify the classes in the training data. The second stage focuses on detecting unknown classes which is achieved through the application of Extreme Value Theory. Thanks to this mathematical approach, successful separation of known and unknown classes is achieved. The utilized data can be evaluated under two different headings. The first one consists of network attack types while the second comprises popular social media traffics. According to the performance evaluation metrics, the proposed procedure demonstrates satisfactory results in both the classification of known classes and the detection of unknown classes.