An Investigation of Multi-objective Genetic Algorithms for Encrypted Traffic Identification
نویسندگان
چکیده
The increasing use of encrypted traffic combined with non-standard port associations makes the task of traffic identification increasingly difficult. This work adopts a multi-objective clustering approach to the problem in which a Genetic Algorithm performs both feature selection and cluster count optimization under a flow based representation. Solutions do not use port numbers, IP address or payload. Performance of the resulting model provides 90% detection 0.8% false positive rates with 13 clusters supported by 14 of the original 38 features.
منابع مشابه
An Investigation of a Multi-objective Genetic Algorithm Applied to Encrypted Traffic Identification
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