Mirza, Ali H.Coşan, Selin2019-02-212019-02-212018-05http://hdl.handle.net/11693/50234Date of Conference: 2-5 May 2018Conference name: 26th Signal Processing and Communications Applications Conference (SIU) 2018In this paper, we introduce a sequential autoencoder framework using long short term memory (LSTM) neural network for computer network intrusion detection. We exploit the dimensionality reduction and feature extraction property of the autoencoder framework to efficiently carry out the reconstruction process. Furthermore, we use the LSTM networks to handle the sequential nature of the computer network data. We assign a threshold value based on cross-validation in order to classify whether the incoming network data sequence is anomalous or not. Moreover, the proposed framework can work on both fixed and variable length data sequence and works efficiently for unforeseen and unpredictable network attacks. We then also use the unsupervised version of the LSTM, GRU, Bi-LSTM and Neural Networks. Through a comprehensive set of experiments, we demonstrate that our proposed sequential intrusion detection framework performs well and is dynamic, robust and scalable.EnglishAutoencodersIntrusion detectionLSTMSequential dataUnsupervised learningConference Paper10.1109/SIU.2018.8404689