Application of deep learning to enhance the accuracy of intrusion detection in modern computer networks

Jafar Majidpour, Hiwa Hasanzadeh

Abstract


Application of deep learning to enhance the accuracy of intrusion detection in modern computer networks were studied in this paper. Deep learning is a subcategory of machine learning and is based on a set of algorithms that attempt to model high-level abstract concepts in the data that process this process with The use of a deep graph, which has several layers of processing, consists of several layers of linear and nonlinear transformations. In other words, it's based on learning to display knowledge and features in the model layers. The identification of attacks in computer networks is divided in to two categories of intrusion detection and anomaly detection in terms of the information used in the learning phase. Intrusion detection uses both routine traffic and attack traffic.Abnormal detection methods attempt to model the normal behavior of the system, and any incident that violates this model is considered to be a suspicious behavior. For example, if the web server, which is usually passive, tries to There are many addresses that are likely to be infected with the worm. The abnormal diagnostic methods are Statistical models, Secure system approach, Review protocol, Check files, Create White list, Neural Networks, Genetic Algorithm,Vector Machines ,decision tree. Our results have demonstrated that our approach offers high levels of accuracy, precision and recall together with reduced training time. In our future work, the first avenue of exploration for improvement will be to assess and extend the capability of our model to handle zero-day attacks.

Keywords


Decision Tree; Deep Learning; Detection in Modern Computer Networks; Genetic Algorithm; Neural Networks; Vector Machines


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