Intrusion Detection and Classification with Autoencoded Deep Neural Network : WestminsterResearch

Publication dates
Chapter title	Intrusion Detection and Classification with Autoencoded Deep Neural Network
Authors	Rezvy, S, Petridis, M, Lasebae, A and Zebin, T.
Abstract	A Network Intrusion Detection System is a critical component of every internet connected system due to likely attacks from both external and internal sources. A NIDS is used to detect network born attacks such as denial of service attacks, malware, and intruders that are operating within the system. Neural networks have become an increasingly popular solution for network intrusion detection. Their capability of learning complex patterns and behaviors make them a suitable solution for differentiating between normal traffic and network attacks. In this paper, we have applied a deep autoencoded dense neural network algorithm for detecting intrusion or attacks in network connection and evaluated the algorithm with the benchmark NSL-KDD dataset. Our results showed an excellent performance with an overall detection accuracy of 99.3{\%} for Probe, Remote to Local, Denial of Service and User to Root type of attacks. We also presented a comparison with recent approaches used in literature which showed a substantial improvement in terms of accuracy and speed of detection with the proposed algorithm.
Keywords	Deep learning
	Secure computing
	Intrusion detection system
	Autoencoder
	Dense neural network
Book title	Innovative Security Solutions for Information Technology and Communications
Page range	142-156
Year	2019
Publisher	Springer
Published	06 Feb 2019
Series	Lecture Notes in Computer Science
ISBN	9783030129422
ISSN	0302-9743
Digital Object Identifier (DOI)	https://doi.org/10.1007/978-3-030-12942-2_12
File	www_secitc_eu_deep_IDS.pdf
Web address (URL)	https://link.springer.com/chapter/10.1007/978-3-030-12942-2_12

Related outputs

Design and implementation of a deep recurrent model for prediction of readmission in urgent care using electronic health records
Zebin, T. and Chaussalet, T.J. 2019. Design and implementation of a deep recurrent model for prediction of readmission in urgent care using electronic health records. 16th IEEE International Conference on Computational Intelligence in Bioinformatics and Computational Biology. Certosa di Pontignano, Siena - Tuscany, Italy 09 - 11 Jul 2019 IEEE . https://doi.org/10.1109/CIBCB.2019.8791466

A deep learning approach for length of stay prediction in clinical settings from medical records
Zebin, T., Rezvy, S. and Chaussalet, T.J. 2019. A deep learning approach for length of stay prediction in clinical settings from medical records. 16th IEEE International Conference on Computational Intelligence in Bioinformatics and Computational Biology. Certosa di Pontignano, Siena - Tuscany, Italy 09 - 11 Jul 2019 IEEE . https://doi.org/10.1109/CIBCB.2019.8791477

Human activity recognition from inertial sensor time-series using batch normalized deep LSTM recurrent networks
Zebin, T., Sperrin, M., Peek, N. and Casson, A.J. 2018. Human activity recognition from inertial sensor time-series using batch normalized deep LSTM recurrent networks. 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Honolulu, HI, USA 18 - 21 Jul 2018 IEEE . https://doi.org/10.1109/EMBC.2018.8513115

Inertial Sensor Based Modelling of Human Activity Classes: Feature Extraction and Multi-sensor Data Fusion Using Machine Learning Algorithms
Zebin, T., Scully, PJ and Ozanyan, K B. 2017. Inertial Sensor Based Modelling of Human Activity Classes: Feature Extraction and Multi-sensor Data Fusion Using Machine Learning Algorithms. in: eHealth 360°. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering Springer. pp. 306-314

Human activity recognition with inertial sensors using a deep learning approach
Zebin, T., Scully, P.J. and Ozanyan, K.B. 2016. Human activity recognition with inertial sensors using a deep learning approach. 2016 IEEE SENSORS. Orlando, FL, USA 30 Oct - 03 Nov 2016 IEEE . https://doi.org/10.1109/ICSENS.2016.7808590

Permalink - https://westminsterresearch.westminster.ac.uk/item/qq053/intrusion-detection-and-classification-with-autoencoded-deep-neural-network

Intrusion Detection and Classification with Autoencoded Deep Neural Network

Related outputs

Share this

Usage statistics

Export as