ICSES Transactions on Computer Networks and Communications
Vol. 1, No. 2, Nov. 2015
A New Technique by Design an Efficient System for Intrusion Detection
a Masjed-Soleiman Branch, Islamic Azad University (I.A.U), Masjed-Soleiman, Iran
Highlights and Novelties
The basic standard of detect of intrusion is based on the assumption that intrusive activities are noticeably different from normal ones and thus are detectable. In past surveys, the capability of fuzzy systems to solve different kinds of problems confirmed. New attacks are emerging every day, detect of intrusion systems play a basic role in identifying possible attacks to the system, and give proper responses. Evolutionary Fuzzy System with the learning capability of Evolutionary Algorithms hybridizes the approximate reasoning method of fuzzy systems. Propose of this paper is to demonstrate the ability of Evolutionary Fuzzy to deal with detect of intrusion classification problem as a new real-world application area. The Evolutionary Fuzzy System would be capable of extracting accurate fuzzy classification in computer network rules to detect normal and intrusive behaviors from network traffic data and applies them. The experimental results were performed with detect of intrusion benchmark dataset which has information on computer networks, and intrusive behaviors during normal. Results of our model have been compared with several famous detect of intrusion systems.
Component intrusion detection simulated annealing search fuzzy if-then rules.
Copyright and Licence
© Copyright was transferred to International Computer Science and Engineering Society (ICSES) by all the Authors. This manuscript is published in Open-Access manner based on the copyright licence of Creative Commons Attribution Non Commercial 4.0 International (CC BY-NC 4.0).
Cite this manuscript as
AbdolHamid MomenZadeh, "A New Technique by Design an Efficient System for Intrusion Detection," ICSES Transactions on Computer Networks and Communications, vol. 1, no. 2, pp. 7-11, Nov. 2015. DOI: 10.1234/icses.itcnc.2018.4.1.-12 (test)
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