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Bidirectional LSTM Autoencoder for Sequence Based Anomaly Detection in Cyber Security
- Source :
- International journal of simulation: systems, science & technology.
- Publication Year :
- 2019
- Publisher :
- UK Simulation Society, 2019.
-
Abstract
- Cyber-security is concerned with protecting information, a vital asset in today’s world. The volume of data that is generated can be usefully analyzed when cyber-security systems are effectively implemented with the aid of software support. Our approach is to determine normal behavior of a system based on sequences of system call traces made by the kernel processes in the system. This paper describes a robust and computationally efficient anomaly based host based intrusion detection system using an Encoder-Decoder mechanism. Using CuDNNLSTM networks, it is possible to obtain a set of comparable results with reduced training times. The Bidirectional Encoder and a unidirectional Decoder is trained on normal call sequences in the ADFA-LD dataset. Intrusion Detection is evaluated based on determining the probability of a sequence being reconstructed by the model yes
Details
- ISSN :
- 1473804X
- Database :
- OpenAIRE
- Journal :
- International journal of simulation: systems, science & technology
- Accession number :
- edsair.doi.dedup.....2de3e4143a477d7bd5a75e6a111c695e
- Full Text :
- https://doi.org/10.5013/ijssst.a.20.05.07