Recently cyber security has emerged as an established discipline for computer systems and infrastructures with a focus on protection of valuable information stored on those systems from adversaries who want to obtain, corrupt, damage, destroy or prohibit access to it. Several information security techniques are available today to protect information systems against unauthorized use, duplication, alteration, destruction and virus attacks. An Intrusion Detection System (IDS) is a program that analyzes what happens or has happened during an execution and tries to find indications that the computer has been misused. This article presents some of the challenges in designing efficient intrusion detection systems which could provide high accuracy, low false alarm rate and reduced number of features. Finally, we present how some of the computational intelligence paradigms could be used in designing intrusion detection systems in a distributed environment.

">

Cyber Security And The Evolution Of Intrusion Detection Systems

Ajith Abraham*, Crina Grosan**, Yuehui Chen***
*School of Computer Sc. & Engg.Chung-Ang University Korea
**Department of Computer Sc.Babes-Bolyai University, Cluj-Napoca 3400, Romania.
***School of Information Sc. & Engg.Jinan University, Jinan 250022 P.R.China
Periodicity:August - October'2005
DOI : https://doi.org/10.26634/jfet.1.1.968

Abstract

Recently cyber security has emerged as an established discipline for computer systems and infrastructures with a focus on protection of valuable information stored on those systems from adversaries who want to obtain, corrupt, damage, destroy or prohibit access to it. Several information security techniques are available today to protect information systems against unauthorized use, duplication, alteration, destruction and virus attacks. An Intrusion Detection System (IDS) is a program that analyzes what happens or has happened during an execution and tries to find indications that the computer has been misused. This article presents some of the challenges in designing efficient intrusion detection systems which could provide high accuracy, low false alarm rate and reduced number of features. Finally, we present how some of the computational intelligence paradigms could be used in designing intrusion detection systems in a distributed environment.

Keywords

How to Cite this Article?

Ajith Abraham, Crina Grosan and Yuehui Chen (2006). Cyber Security And The Evolution Of Intrusion Detection Systems. i-manager’s Journal on Future Engineering and Technology, 1(1), 74-82. https://doi.org/10.26634/jfet.1.1.968

References

[1]. Denning D., An Intrusion-Detection Model, IEEE Transactions on Software Engineering, Vol. SE-13, No. 2, pp.222-232, 1987.
[2]. Brieman L., Friedman J., Olshen R., and Stone C., Classification of Regression Trees. Wadsworth Inc., 1984.
[3]. Lee W. and Stolfo S. and Mok K., A Data Mining Framework for Building Intrusion Detection Models. In Proceedings of the IEEE Symposium on Security and Privacy, 1999. MIT Lincoln Laboratory. Http://www.ll.mit. edu/IST/ideval/
[4]. J H Friedman, Multivariate Adaptative Regression Splines. Annals of Statistics Vol. 19 1991.
[5]. Mukkamala S., Sung A. and Abraham A., Intrusion Detection Using Ensemble of Soft Computing and Hard Computing Paradigms, Journal of Network and Computer Applications, Elsevier Science, Vol. 28, Issue 2, pp. 167-182, 2005.
[6]. Srilatha Chebrolu, Ajith Abraham and Johnson Thomas, Feature Deduction and Ensemble Design of Intrusion Detection Systems, Computers and Security, Elsevier Science, Vol. 24/4, pp. 295-307, 2005.
[7]. Ajith Abraham and Johnson Thomas, Distributed Intrusion Detection Systems: A Computational Intelligence Approach, Applications of Information Systems to Homeland Security and Defense, Abbass H.A. and Essam D. (Eds.), Idea Group Inc. Publishers, USA, Chapter 5, pp. 105-135, 2005.
[8]. Yuehui Chen and Ajith Abraham and Ju Yang, Feature Deduction and Intrusion Detection Using Flexible Neural Trees, Second IEEE International Symposium on Neural Networks (ISNN 2005), Lecture Notes in Computer Science Vol. 3498, J. Wang, X. Liao and Zhang Yi (Eds.) Springer Verlag, Germany, pp. 439 - 446, 2005.
[9]. Vitorino Ramos and Ajith Abraham, ANTIDS: Self Organized Ant Based Clustering Model for Intrusion Detection System, The Fourth IEEE International Workshop on Soft Computing as Transdisciplinary Science and Technology (WSTST'05), Japan, Springer Verlag, Germany, pp. 977-986, 2005.
[10]. Crina Grosan, Ajith Abraham and Sang Yong Han, MEPIDS: Multi-Expression Programming for Intrusion Detection System, International Work-conference on the Interplay between Natural and Ar tificial Computation, (IWINAC'05), Spain, Lecture Notes in Computer Science, LNCS 3562, J. Mira and J.R. Alvarez (Eds.), Springer Verlag, Germany, pp. 163172, 2005.
If you have access to this article please login to view the article or kindly login to purchase the article

Purchase Instant Access

Single Article

North Americas,UK,
Middle East,Europe
India Rest of world
USD EUR INR USD-ROW
Pdf 35 35 200 20
Online 35 35 200 15
Pdf & Online 35 35 400 25

Options for accessing this content:
  • If you would like institutional access to this content, please recommend the title to your librarian.
    Library Recommendation Form
  • If you already have i-manager's user account: Login above and proceed to purchase the article.
  • New Users: Please register, then proceed to purchase the article.