Gated Recurrent Units for Intrusion Detection

As the arms race between the new kinds of attacks and new ways to detect and prevent those attacks continues, better and better algorithms have to be developed to stop the malicious agents dead in their tracks. In this paper, we evaluate the use of one of the youngest additions to the deep learning architectures, the Gated Recurrent Unit for its feasibility in the intrusion detection domain. The network and its performance is evaluated with the use of a well-established benchmark dataset, called NSL-KDD. The experiments, with the accuracy surpassing the average of 98%, proves that GRU is a viable architecture for intrusion detection, achieving results comparable to other state-of-the-art methods.