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Intrusion detection is an important security technology, but the existing intrusion detection technology exists a variety of deficiencies , and only sword known intrusion detection can not detect new, unknown intrusion . Based on rough set and artificial immune intrusion detection using rough set attribute reduction theory , drawing on the biological immune mechanism , fill the existing intrusion detection deficiencies , better adapted to the current complex network environment , with great research and application value. First , the paper intrusion detection concepts and theories related to the research and analysis, summarized the classification of intrusion detection and commonly used methods. Secondly , the paper on rough set theory and artificial immune systems on the basis of research , proposed a rough set and artificial immune intrusion detection model . Paper a detailed analysis of the structure and composition of the model , and the model of the key technologies in-depth study . By introducing artificial immune \Rough set theory constructed by using system calls the decision table , and the decision table reduction, elimination of redundant attributes in decision table information , from the reduction in the properties of extracted rules , and ultimately construct a \self- rule base \Application of artificial immune related principles and algorithms to generate SELF and NOSELF detector makes intrusion detection has better dynamic adaptability . Finally, Forest , who provided normal and abnormal system call data, based on rough set and artificial immune intrusion detection model simulation experiments . The experimental results show that the model can accurately identify normal and abnormal behavior , can reduce the false alarm rate and improve the detection rate , the intrusion detection model is feasible.
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