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812 JOURNAL OF SOFTWARE, VOL. 6, NO. 5, MAY 2011 An antigen was defined as a fixed length binary string composed of the source/destination IP address, port number, protocol type, IP flags, IP overall packet length, TCP/UDP/ICMP fields, and etc. The network was attacked by 20 kinds of attacks, such as Syn Flood, Land, Smurf, and Teardrop. A total of 20 computers in a network were under surveillance. The task aimed to detect network attacks. Here are the coefficients for the model. We use r-contiguous bits matching rule (r=8) for computing the affinity, n=40 (the size of initial self set), and ξ =4 (the number of new generated immature detectors). The activation threshold is β ; tolerance period is λ . B. Results and Analysis Figure 3 illustrates the syn attacks. Figure 4 depicts the evaluation of the network danger in our model. Packets/s 30000 20000 10000 0 0 20 40 60 80 100 120 Time Figure 3. The network suffering from the syn incursions for instance Attack intensity 1 0.8 0.6 0.4 0.2 0 0 20 40 60 80 100 120 Time Figure 4. The line of the network dangers obtained by our model at these incursions As is shown in Figure 4, R(t) changes when attack levels changes. The rise in attack levels is accompanied by a corresponding increase in R(t), as implies the bad network security. On the other hand, if attack levels decline, R(t) decreases accordingly after seconds of delay. Therefore, the network can stays on guard even when the attacks occur once again during a very short time. © 2011 ACADEMY PUBLISHER Ⅵ.CONCLUSIONS This paper combines the risk evaluation methods with application security engineering principles, and can change current passive defense situation using traditional network security approaches, and is helpful to establish new generation proactive defense theories and realization techniques. At the same time, the work is of not only theoretic values to design proactive defense systems which have intrusion tolerant ability and survivability in any complex network circumstances, but also very significant to protect network infrastructure. The experimental results show that the proposed model has the features of real-time processing that provide a good solution for network surveillance. ACKNOWLEDGMENT This work is supported by the National Natural Science Foundation of China under Grant (No.61003310) and the Scientific Research Fund of Sichuan Provincial Education Department (No. 10ZB005). REFERENCES [1] Thomas, Ciza, Balakrishnan, N. Performance enhancement of Intrusion Detection Systems using advances in sensor fusion. Information Fusion, 2008 11 th International Conference on June 30 , (2008):1-7 [2] Abdoul Karim Ganame, Julien Bourgeois, Renaud Bidou, Francois Spies. A global security architecture for intrusion detection on computer networks. Computers & Security, 27(1), 2008:30-47 [3] Vasilios Katos. Network intrusion detection: Evaluating cluster, discriminant, and logit analysis. Information Sciences, 177(15), (2007), 3060-3073. [4] Agustín Orfila, Javier Carbó, Arturo Ribagorda. Autonomous decision on intrusion detection with trained BDI agents. Computer Communications, 31(9), (2008),1803-1813. [5] Vincent Toubiana, Houda Labiod, Laurent Reynaud, Yvon Gourhant. A global security architecture for operated hybrid WLAN mesh networks. Computer Networks, 54(2),2010: 218-230 [6] Kuby J.: Immunology. Fifith Edition by Richard A. Goldsby et al. [7] F.M.Burnet. The Clone Selection Theory of Acquired Immunity. Gambridge: Gambridge University Press (1959) [8] S A Hofmeyr, and S Forrest. Architecture for an artificial immune system. Evolutionary Computation, vol. 8 (2000) 443-473 [9] S Forrest, A S Perelson, L Allen, and R Cherukuri. Self- Nonself Discrimination in a Computer. Proceedings of IEEE Symposium on Re-search in Security and Privacy, Oakland, (1994) [10] Panigrahi BK, Yadav SR, Agrawal S, et al. A clonal algorithm to solve economic load dispatch. Electric Power Systems Research. 77(10), (2007):1381-1389 [11] Tao Li. An immune based dynamic intrusion detection model. Chinese Science Bulletin. 50, (2005): 2650-2657
JOURNAL OF SOFTWARE, VOL. 6, NO. 5, MAY 2011 813 [12] Tao Li. An immunity based network security risk estimation. Science in China Ser. F Information Sciences. 48 (2005):557- 578 © 2011 ACADEMY PUBLISHER Jin Yang received his M.S. degree and the Ph.D. degree in computer science from Sichuan University, Sichuan, China. He is an Associate Professor in Department of Computer Science at LeShan normal university. His main research interests include network security, artificial immune, knowledge discovery and expert systems. Tang Liu received his M.S. degree in College of Computer Science, Sichuan University, China, in 2009. Since 2008, he has been a instructor in College of Fundamental Education, Sichuan Normal University. His research interests are in the area of wireless sensor networks. Lingxi Peng born in 1978. Associate professor, Ph.D. and senior membership of China. His main research interests include network security and artificial immune.
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