Titlebook: Artificial Immune Systems; 7th International Co Peter J. Bentley,Doheon Lee,Sungwon Jung Conference proceedings 2008 Springer-Verlag Berlin

學(xué)術(shù)討論會

注意

Essential Areas in Empirical Research and network monitoring applications into an innate immune capability inspired by the immunological Danger Model. Experimentation on a prototype NetTRIIAD implementation demonstrates improved detection accuracy in comparison with misuse-based intrusion detection. Areas for future investigation and improvement to the model are also discussed.

轉(zhuǎn)向

ADORE

LAVE

Experiment Process Illustration”. The model proposes a system based on two stochastic networks: a central recursive network, wherein the proportion of agents is determined and maintained, and a peripheral network, wherein the random interactions of these agents determine if an inflammatory response will emerge from the system.

VAN

Herrade Igersheim,Mathieu Lefebvreed on results of conducted experiments, the obtained classification accuracy of proposed system is 88.6 % using 10 fold cross validation for TMM genre classification. The results also show that AIRS with nonlinear allocation method obtains maximum classification accuracy for TMM genre classification.

CON

Kennzeichen chemischer Reaktionen,n multiplier and moving time windows have the same effect on the DCA for this particular data set. The real-valued NSA with contant-sized detectors is not applicable to the data set. And the C4.5 decision tree algorithm provides a benchmark of the classification performance for this data set.

mortgage

Zur Logik experimenteller Forschung,rawback, the set of tests conducted with the proposed approach indicates a good performance of the algorithm when compared with similar works from the literature and the known best solutions available.

Grating

watertight,

Artificial Immune Recognition System with Nonlinear Resource Allocation Method and Application to Tred on results of conducted experiments, the obtained classification accuracy of proposed system is 88.6 % using 10 fold cross validation for TMM genre classification. The results also show that AIRS with nonlinear allocation method obtains maximum classification accuracy for TMM genre classification.