| Title | Computer network intrusion detection using sequential LSTM Neural Networks autoencoders |
| Publication Type | Conference Paper |
| Year of Publication | 2018 |
| Authors | Mirza, Ali H., Cosan, Selin |
| Conference Name | 2018 26th Signal Processing and Communications Applications Conference (SIU) |
| Keywords | autoencoders, Bi-LSTM, composability, computer network data, computer network intrusion detection, computer network security, computer networks, Decoding, encoding, feature extraction, fixed length data sequence, GRU, Intrusion detection, Iterative methods, long short term memory neural network, LSTM, Metrics, network data sequence, network intrusion detection, neural nets, Neural networks, Payloads, pubcrawl, Resiliency, sequential autoencoder, sequential data, sequential intrusion detection, sequential LSTM Neural Networks autoencoders, Training, unsupervised learning, variable length data sequence |
| Abstract | In 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. |
| DOI | 10.1109/SIU.2018.8404689 |
| Citation Key | mirza_computer_2018 |
Computer network intrusion detection using sequential LSTM Neural Networks autoencoders