The concept of Virtual Paths (VP) is a powerful technique to improve the transmission efficiency in ATM networks. Transmission efficiency can be improved by dynamically changing the bandwidths of the VPs, based on the demand. Intelligent controllers, which predict bandwidth-demand patterns to enable better VP management, have the potential to revolutionize ATM network performance. We present a scheme based on the Evolutionary Genetic Approach to predict the bandwidth-demand patterns in VPs. The efficiency of this approach, quantified in terms of the Degree of Learning (DoL), is evaluated through simulation and the results are presented.

Serugudi V. Raghavan

Asynchronous transfer mode

Bandwidth

Computer networks

Congestion control (communication)

Genetic algorithms

Intelligent control

Intelligent networks

Learning algorithms

Telecommunication traffic

BANDWIDTH DEMAND PREDICTION

VIRTUAL PATHS (VP)

Data communication systems

IIT Madras is a public technical and research university located in Chennai, Tamil Nadu. Founded in 1959, it is recognised as an Institute of National Importance.

IIT Madras has been ranked as the top engineering institute in India for four years in a row by the National Institutional Ranking Framework of the MHRD

It currently offers undergraduate, postgraduate and research degrees across 16 disciplines in Engineering, Sciences, Humanities and Management. About 596 faculty belonging to science and engineering departments and centres of the Institute are engaged in teaching, research and industrial consultancy.

IIT Madras

Bandwidth-demand prediction in virtual path in ATM networks using genetic algorithms

Computer Communications

Intrusion detection is the process of identifying user actions that might potentially lead a system from a secured state to a compromised state. Normally, it is observed that the users exhibit regularities in their usage of commands of a system, as they tend to achieve the same (or perhaps similar) objective. The command sequences can therefore be used to characterize the user behavior (ACM SIGMETRICS, Performance Evaluation Review, Texas, USA, 13(2) (1985) 40). Deviations from the characteristic behavior pattern of a user can be used to detect potential intrusions. But, it requires that the user behavior is modeled either on an individual or on a group basis, in such a way that the model captures the essence of the user behavior. In this work reported here, we propose an algorithm for intrusion detection, called Genetic algorithm Based Intrusion Detector (GBID) based on "learning the individual user behavior". The user behavior is learnt by using genetic algorithms. Current user behavior can be predicted by genetic algorithms based on the past observed user behavior. The user behavior has been described using a 3-tuple 〈Match index, Entropy index, Newness index〉. Value of the 3-tuple is calculated for fixed block size of commands in a user session, called command sample. The 3-tuple value of a command sample in user session are compared with expected non-intrusive behavior 3-tuple value to find intrusions. © 2001 Elsevier Science B.V.

Intrusion detection through learning behavior model

Choice Reviews Online

Genetic algorithms in search, optimization, and machine learning

IEEE/ACM Transactions on Networking

Wide area traffic: the failure of Poisson modeling

Practical connection admission control for ATM networks based on on-line measurements

Virtual path control for ATM networks with call level quality of service guarantees

Electronics Letters

Learning algorithms for quality of service multicast routing

Journal	Data powered by TypesetComputer Communications
Publisher	Data powered by TypesetElsevier Science B.V., Amsterdam
ISSN	01403664
Open Access	No