References
[1]. Chebrolu S, Abraham A, Thomas JP, (2005). “Feature
Detection and Ensemble Design of Intrusion Detection
Systems,” Computer Security, Vol.24, pp.295–307.
[2]. Chen Y, Abraham A, Yang J, (2005). “Feature
Deduction and Intrusion Detection Using Flexible Neural
nd Trees”, In 2 IEEE International Symposium on Neural
Networks, pp.1-4.
[3]. Chen, ZL. Gao, and K. Kwiat, (2003). “Modeling the
nd Spread of Active Worms”, Proceedings of 22 Annual
Joint Conference of the IEEE Computer and
Communications Societies, pp.1890-1900.
[4]. Chou T.S, Yen KK, (2007). “Fuzzy Belief K-Nearest
Neighbors Anomaly Detection of User to Root and Remote
to Local Attacks”, IEEE Workshop on Information
Assurance, pp.207–213.
[5]. Cohen F, (1987). ”Computer Viruses: Theory and
Experiments,” Computer Security, Vol.6(1), pp.22–35.
[6]. Ghosh AK, Schwartzbard A, (2004), “A Study in Using
Neural Networks for Anomaly and Misuse Detection,” In
Usenix Security Symposium, Washington, DC.
[7]. Ghosh AK, Schwartzbard A, Schatz M, (1999).
“Learning program Behavior Profiles for Intrusion
Detection,” Workshop on Intrusion Detection and Network
Monitoring, Santa Clara, CA, USENIX.
[8]. Livadas C, Walsh B, Lapsley D, Strayer T, (2006). “Using
Machine Learning Techniques to Identify Botnet Traffic,”
nd 2 IEEE LCN workshop on network security (WNS), Tampa,
FL, USA.
[9]. Meystel, A.M. and Albus, J.M, (2002). “Intelligent
Systems Architecture, Design, and Control,” New York,
New York, John Wiley & Sons, Inc.
[10]. Mukkamala S, Janoski G, Sung A, (2001).
“Monitoring Systsem Security Using Neural Networks and
Support Vector Machines,” International Workshop on
Hybrid Intelligent Systems, pp.121–138.
[11]. Mukkamala S, Sung A, Abraham, (2004). “Designing
Intrusion Detection Systems: Architectures and
Perspectives”, The International Engineering Consortium
(IEC) Annual Review of Communications, Vol.57,
pp.1229–1241.
[12]. Mukkamala S, Sung A, Abraham, (2007). “Hybrid
Multi-agent Framework for Detection of Stealthy Probe,”
Appl Soft Computing, Vol.7(3), pp.631–641.
[13]. Mukkamala S, Sung AH, Abraham A, (2003).
“Intrusion Detection Using Ensemble of Soft Computing
rd Paradigms,” 3 International Conference on Intelligent
Systems Design and Applications, Advances in Soft
Computing, pp.239–248.
[14]. Mukkamala S, Sung AH, Abraham A, (2004).
“Modeling Intrusion Detection Systems Using Linear
th Genetic Programming Approach”, 17 International
Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, Vol.30(29),
pp.633–642.
[15]. Ourston D, Matzner S, Stump W, Hopkins B, (2004).
“Coordinated Internet Attacks: Responding to Attack
Complexity”, Computer Security, Vol.12, pp.165–190.
[16]. Shah.K, Dave N, Chavan S, Mukherjee S, Abraham
A, Sanyal S, (2004). “Adaptive Neuro-fuzzy Intrusion
Detection System”, IEEE International Conference on
ITCC'04, Vol.1, pp.70–74.
[17] . Wang. W, Gombault S, Guyet T, (2008). ”Towards Fast
Detecting Intrusions: Using Key Attributes of Network
rd Traffic,” The 3 International Conference on Internet
Monitoring and Protection, IEEE Press, New York,
pp.86–91.
18]. Wang. J, (2007). “Internet Worm Early Detection and
Response Mechanism,” The Journal of China Universities
of Posts and Telecommunications, Vol.14(3).
