Abstract
In order to find the attack in real time, an intrusion prediction method based on intelligent immune threshold matching algorithm was presented. Using a dynamic load-balancing algorithm, network data packet was distributed to a set of predictors by the balancer; it could avoid packet loss and false negatives in high-performance network with handling heavy traffic loads in real-time. In addition, adopting the dynamic threshold value, which was generated from variable network speed, the mature antibody could better match the antigen of the database, and consequently the accuracy of prediction was increased. Experiment shows this intrusion prediction method has relatively low false positive rate and false negative rate, so it effectively resolves the shortage of intrusion detection.
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Cao, LC. (2010). Enhancing Efficiency of Intrusion Prediction Based on Intelligent Immune Method. In: Huang, DS., Zhang, X., Reyes GarcÃa, C.A., Zhang, L. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence. ICIC 2010. Lecture Notes in Computer Science(), vol 6216. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14932-0_74
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DOI: https://doi.org/10.1007/978-3-642-14932-0_74
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