A Neural Network Approach for Intrusion Detection Systems

Conference paper


Beqiri, Elidon, Lee, S. and Draganova, Chrisina 2010. A Neural Network Approach for Intrusion Detection Systems. 5th Conference in Advances in Computing and Technology (London, United Kingdom, 27th Jan), pp. 209 -217
AuthorsBeqiri, Elidon, Lee, S. and Draganova, Chrisina
TypeConference paper
Abstract

Intrusion detection systems, alongside firewalls and gateways, represent the first line of defense against computer network attacks. There are various commercial or open source intrusion detection systems in the market; nevertheless they do not perform well in various situations including novel attacks, user activity detection, generating in some cases false positive or negative alerts. The reason behind such performance is probably due to the implementation of merely signature based checks and a high degree of dependence on human interaction. On the other hand, a neural network approach might be the right one to tackle these issues. Neural networks have already been applied successfully to solve many problems related to pattern recognition, data mining, data compression and research is still underway with regards to intrusion detection systems. Unsupervised learning and fast network convergence are some features that can be integrated into an IDS system using neural networks. The networks can be designed to process a variety of data, although there are some constraints regarding input formatting. For this reason, data encoding represents a challenging task in the integration process since it needs to be optimised for the IDS domain. This paper will discuss the integration of IDS and neural networks, including data encoding and performance issues.

KeywordsNeural network; snap-drift; intrusion detection system; encoding; performance
Year2010
Conference5th Conference in Advances in Computing and Technology (London, United Kingdom, 27th Jan), pp
Publication dates
PrintJan 2010
Publication process dates
Deposited23 Feb 2010
Web address (URL)http://www.uel.ac.uk/act/proceedings/index.htm
http://hdl.handle.net/10552/619
Additional information

Citation:
Beqiri, E.; Lee. S. W.; Draganova, C. and Palmer-Brown, D. (2010) ‘A Neural Network Approach for Intrusion Detection Systems’, 5th Conference in Advances in Computing and Technology (London, United Kingdom, 27th Jan), pp. 209 -217..

File
License
CC BY-ND
File
Permalink -

https://repository.uel.ac.uk/item/8628y

  • 0
    total views
  • 0
    total downloads
  • 0
    views this month
  • 0
    downloads this month

Related outputs

Activities of daily life recognition using process representation modelling to support intention analysis
Naeem, U., Bashroush, R., Anthony, Richard, Azam, Muhammad Awais, Tawil, Abdel Rahman, Lee, S. and Mou-Ling, Dennis 2015. Activities of daily life recognition using process representation modelling to support intention analysis. International Journal of Pervasive Computing and Communications. 11 (3), pp. 347-371.
Direct state feedback optimal control of a double integrator plant implemented by an artificial neural network
Matieni, Xavier, Dodds, Stephen J. and Lee, S. 2011. Direct state feedback optimal control of a double integrator plant implemented by an artificial neural network. Advances in Computing and Technology. University of East London, London Jan 2011 London University of East London, School of Architecture Computing and Engineering.
Closed-loop control using a backpropagation algorithm: a practicable approach for energy loss minimisation in electrical drives.
Matieni, Xavier, Dodds, Stephen J. and Lee, S. 2010. Closed-loop control using a backpropagation algorithm: a practicable approach for energy loss minimisation in electrical drives. Proceedings of Advances in Computing and Technology, (AC&T) The School of Computing and Technology 5th Annual Conference, University of East London, pp. 72-78
Question response grouping for online diagnostic feedback
Lee, S., Palmer-Brown, Dominic, Draganova, Chrisina, Preston, David and Kretsis, Mike 2009. Question response grouping for online diagnostic feedback. Proceedings of Advances in Computing and Technology. (AC&T) The School of Computing and Technology 4th Annual Conference University of East London pp. 68-76
Automated updating of road network databases: road segment grouping using snap-drift neural network
Ekpenyong, Frank, Brimicombe, Allan J., Palmer-Brown, Dominic, Li, Yang and Lee, S. 2007. Automated updating of road network databases: road segment grouping using snap-drift neural network. Proceedings of Advances in Computing and Technology. (AC&T) The School of Computing and Technology 2nd Annual Conference University of East London pp. 160-167
An assessment of neural network algorithms that could aid SME survival
Walcott, Terry H., Palmer-Brown, Dominic, Williams, Godfried, Mouratidis, Haralambos and Lee, S. 2007. An assessment of neural network algorithms that could aid SME survival. Proceedings of Advances in Computing and Technology. (AC&T) The School of Computing and Technology 2nd Annual Conference University of East London pp. 120-127
Feature discovery using snap-drift neural networks
Lee, S. and Palmer-Brown, Dominic 2007. Feature discovery using snap-drift neural networks. Proceedings of Advances in Computing and Technology. (AC&T) The School of Computing and Technology 2nd Annual Conference University of East London pp. 61-70
Modal Learning in a Neural Network
Lee, S. and Palmer-Brown, Dominic 2006. Modal Learning in a Neural Network. Proceedings of the AC&T, pp. 42-47
Performance-guided Neural Network for Self-Organising Network Management
Lee, S., Palmer-Brown, Dominic, Tepper, Jonathan and Roadknight, Christopher 2002. Performance-guided Neural Network for Self-Organising Network Management. Proceedings of London Communication Symposium (LCS'2002) University College London, London, UK, 9th – 10th September, pp. 269 - 272
Fast Learning Neural Nets with Adaptive Learning Styles
Palmer-Brown, Dominic, Lee, S., Tepper, Jonathan and Roadknight, Chris 2003. Fast Learning Neural Nets with Adaptive Learning Styles.
Snap-Drift: Real-time, Performance-guided Learning
Lee, S., Palmer-Brown, Dominic, Tepper, Jonathan and Roadknight, Christopher 2003. Snap-Drift: Real-time, Performance-guided Learning.
Continuous Reinforced Snap-Drift Learning in a Neural Architecture for Proxylet Selection in Active Computer Networks
Palmer-Brown, Dominic and Lee, S. 2005. Continuous Reinforced Snap-Drift Learning in a Neural Architecture for Proxylet Selection in Active Computer Networks. International Journal on Simulation: Systems, Science and Technology. 6 (9), pp. 11-21.
The Analysis of Network Manager’s Behaviour using a Self-Organising Neural Networks
Palmer-Brown, Dominic and Lee, S. 2005. The Analysis of Network Manager’s Behaviour using a Self-Organising Neural Networks. International Journal on Simulation: Systems, Science and Technology. 6 (9), pp. 22-32.
Phonetic Feature Discovery in Speech using Snap-Drift
Lee, S. and Palmer-Brown, Dominic 2006. Phonetic Feature Discovery in Speech using Snap-Drift.
Early SME Market Prediction using USDNN
Walcott, Terry H., Palmer-Brown, Dominic and Lee, S. 2008. Early SME Market Prediction using USDNN. in: Proceedings of the International Conference of Computational Intelligence and Intelligent Systems (ICCIIS'2008) International Association of Engineers.
Diagnostic Feedback by Snap-drift Question Response Grouping
Lee, S., Palmer-Brown, Dominic and Draganova, Chrisina 2008. Diagnostic Feedback by Snap-drift Question Response Grouping. in: Proceedings of 9th WSEAS International Conference on Neural Networks (NN'08) Stevens Point (WI), USA World Scientific and Engineering Academy and Society. pp. 208-214
Modal Learning Neural Networks
Palmer-Brown, Dominic, Lee, S., Draganova, Chrisina and Kang, Miao 2009. Modal Learning Neural Networks.
Snap-Drift Neural Network for Selecting Student Feedback
Palmer-Brown, Dominic, Draganova, Chrisina and Lee, S. 2009. Snap-Drift Neural Network for Selecting Student Feedback. International Joint Conference on Neural Networks, IJCNN 2009. Atlanta, Georgia, USA 14 - 19 Jun 2009 IEEE.