The Integrated Artificial Immune Intrusion Detection Model Based on Decision-theoretic Rough Set

Rui-Hong Dong ； Dong-Fang Wu ； Qiu-Yu Zhang

《International Journal of Network Security》 19卷6期 (2017/11) Pp. 880-888

https://doi.org/10.6633/IJNS.201711.19(6).03

摘要

The intrusion detection methods used in the industrial control network generally have a higher false positive rate. Considering this issue and improving the detection performance of intrusion behaviors, an integrated artificial immune intrusion detection model based on decision- theoretic rough set was proposed in this paper. Firstly, by the approach of decision-theoretic rough set attributes reduction algorithm (DTRSA), attributes reduction was finished. And the rule set was obtained from the training data which has the binary string form. Secondly, taking into consideration of the negative selection algorithm (NSA), the rule set produced the corresponding detector sets. Vaccine mechanism was added into the model. Finally, real time dendritic cell algorithm (rtDCA) analyzed the environment and antigen information. The antigen matching threshold was obtained. Considering the intrusion behaviors and antigen matching threshold, the dynamic increases of rule set was achieved. Experimental results show that the proposed model obtained the lower false positive rate (FP) and the true positive rate (TP) reached to 95.5%. And both known and unknown intrusion detections had the high performance.

關鍵字

Decision-theoretic Rough Set ； Detector ； In-tegrated Artificial Immune ； Intrusion Detection System ； Rule Set