IEEE Transactions on Software Engineering - Special issue on computer security and privacy
Elements of artificial neural networks
Elements of artificial neural networks
Mlp neural network and on-line backpropagation learning implementation in a low-cost fpga
Proceedings of the 18th ACM Great Lakes symposium on VLSI
Artificial neural network approaches to intrusion detection: a review
TELE-INFO'09 Proceedings of the 8th Wseas international conference on Telecommunications and informatics
Application of artificial neural network in detection of DOS attacks
Proceedings of the 2nd international conference on Security of information and networks
Comparative Analysis of Intrusion Detection Approaches
UKSIM '10 Proceedings of the 2010 12th International Conference on Computer Modelling and Simulation
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The duty of securing networks is very difficult due to their size, complexity, diversity and dynamic situation. Currently applying neural networks in intrusion detection is a robust approach to ensure security in the network system. Further, neural networks are alternatives to other approaches in the area of intrusion detection. The main objective of this research is to present an adaptive, flexible and optimize neural network architecture for intrusion detection system that provides the potential to identify network activity in a robust way. The results of this work give directions to enhance security applications such as Intrusion Detection System (IDS), Intrusion Prevention System (IPS), Adaptive Security Alliance (ASA), check points and firewalls and further guide to the security implementers.