Handbook of image processing operators
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Applying data mining to intrusion detection: the quest for automation, efficiency, and credibility
ACM SIGKDD Explorations Newsletter
Complex-Valued Neural Networks: Theories and Applications (Series on Innovative Intelligence, 5)
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IEEE Transactions on Neural Networks
Fast image matching on web pages
CIS'09 Proceedings of the international conference on Computational and information science 2009
Design of anti-GPS for reasons of security
CIS'09 Proceedings of the international conference on Computational and information science 2009
Fast information processing over business networks
AIC'09 Proceedings of the 9th WSEAS international conference on Applied informatics and communications
Fast image matching on web pages
WSEAS Transactions on Signal Processing
Fast harmonic current/voltage prediction by using high speed time delay neural networks
CIT'09 Proceedings of the 3rd International Conference on Communications and information technology
Fast intrusion detection by using high speed focused time delay neural networks
CIT'09 Proceedings of the 3rd International Conference on Communications and information technology
Identity based threshold cryptography and blind signatures for electronic voting
WSEAS Transactions on Computers
Fast human motion tracking by using high speed neural
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EHAC'12/ISPRA/NANOTECHNOLOGY'12 Proceedings of the 11th WSEAS international conference on Electronics, Hardware, Wireless and Optical Communications, and proceedings of the 11th WSEAS international conference on Signal Processing, Robotics and Automation, and proceedings of the 4th WSEAS international conference on Nanotechnology
A new hybrid system for information security
ACA'12 Proceedings of the 11th international conference on Applications of Electrical and Computer Engineering
A new fast neural network model
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RT-MOVICAB-IDS: Addressing real-time intrusion detection
Future Generation Computer Systems
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E-government is an important issue which integrates existing local area networks into a global network that provide many services to the nation citizens. This network requires a strong security infrastructure to guarantee the confidentiality of national data and the availability of government services. In this paper, a framework for network intrusion detection systems is presented. Such framework utilizes data mining techniques and is customized for the E-Government Network (EGN). It consists of two phases: an offline phase in which the intrusion detection system learns the normal usage profiles for each local network domain, and a real time intrusion detection phase. In the real time phase, known attacks are detected at a global layer at the EGN perimeters while normal behavior is filtered out at a local layer defined for each LAN domain. Clustering is used to focus the analysis on the remaining suspicious activity and identify whether it represents new intrusive or normal behavior. This framework is intended to detect intrusions in real-time, achieve low false alarm rates, and continuously adapt to the environment changes and emergence of new behavior. This research is a development for the work presented in [22,23]. The main achievement of this paper is the fast attack detection algorithm. Such algorithm based on performing cross correlation in the frequency domain between data traffic and the input weights of fast time delay neural networks (FTDNNs). It is proved mathematically and practically that the number of computation steps required for the presented FTDNNs is less than that needed by conventional time delay neural networks (CTDNNs). Simulation results using MATLAB confirm the theoretical computations.