By Rolf Michaelis
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Artiﬁcial Intelligence 97(1-2), 273–324 (1997) 44. : A survey on intrusion detection in mobile ad hoc networks. Wireless/Mobile Network Security, 159–180 (2007) 45. : Intrusion detection in wireless ad hoc networks. IEEE Wireless Communications 11(1), 48–60 (2004) 46. : Intrusion Detection Techniques for Mobile Wireless Networks. Wireless Networks 9(5), 545–556 (2003) 47. : Computing the state of specknets: Further analysis of an innate immune-inspired model. , Jung, S. ) ICARIS 2008. LNCS, vol.
Szczerbicka priming _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ co−stimulation G s 1 s1 F1 o f0 UUU UUUclassif err. prop. UUUU ication UUB G G class. err. f0s2 F1s2 o thresh .. iiR i i i . ii G sn iiii f0 F1sn o (a) An approach with error propagation and co-stimulation. priming _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ F1sn thresh o co−stimulation err. prop. G f0sn classif ication o G class. err. optimization (b) An approach extended with optimization. Fig. 8. Flow graph of the error propagation algorithm.
Of International Symposium on Performance Evaluation of Computer and Telecommunication Systems (SPECTS 2008), pp. 44–51 (2008) 26 M. Drozda, S. Schaust, and H. Szczerbicka 39. : Scalable wireless ad hoc network simulation. In: Handbook on Theoretical and Algorithmic Aspects of Sensor, Ad hoc Wireless, and Peer-to-Peer Networks, pp. 297–311 (2005) 40. : Sasha: toward a self-healing hybrid sensor network architecture. In: EmNets 2005: Proceedings of the 2nd IEEE workshop on Embedded Networked Sensors, Washington, DC, USA, pp.