Anomaly Detection based on Fuzzy Rules

doi:10.23940/ijpe.18.02.p19.376385

Abstract

Abstract:

Essentially, the fuzzy assert rule library is the fuzzy decision tree. A fuzzy decision tree growth algorithm based on local dynamic optimization is present. Following the idea of the greedy strategy, the algorithm ensures that once a continuous attribute is chosen as a branch node, the membership functions of this attribute after fuzzifying is dynamically optimized. On the other hand, according to fuzzy logic, enhanced Apriori algorithm is present to all the fuzzy frequent item sets composed of fuzzified attributes of multiple events. Then, the fuzzy frequent item sets are transformed into fuzzy association rules that compose the fuzzy association rule library. As for a multiple event sequence, eight different detection algorithms are provided and tested on the same platform. Experiments show that two new algorithms using the fuzzy decision tree and fuzzy association rule library detection models get the highest score.

Wenjiang Jiao and Qingbin Li. Anomaly Detection based on Fuzzy Rules [J]. Int J Performability Eng, 2018, 14(2): 376-385.

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