論文提出了基于密度的異常挖掘新方法，并將其應(yīng)用于入侵檢測(cè)系統(tǒng)引擎設(shè)計(jì)中，構(gòu)
建了相應(yīng)的網(wǎng)絡(luò)入侵檢測(cè)系統(tǒng)。該系統(tǒng)通過(guò)挖掘異常數(shù)據(jù)的高效性，可及時(shí)發(fā)現(xiàn)新的未知入侵行為，用以更新入侵規(guī)則庫(kù)。基于該規(guī)則庫(kù)，系統(tǒng)采用BM 模式匹配算法進(jìn)行實(shí)時(shí)入侵檢測(cè)。論文運(yùn)用形式化語(yǔ)言對(duì)入侵檢測(cè)系統(tǒng)各子模塊進(jìn)行結(jié)構(gòu)化分析與描述。
關(guān)鍵詞：異常挖掘；密度；入侵檢測(cè)
Abstract：The paper puts forward a new method of density-based anomaly data mining, the
method is used to design the engine of network intrusion detection system (NIDS), thus a new IDS is constructed based on the engine. The NIDS can find new unknown intrusion behaviors, hich are used to updated the intrusion rule-base, based on which intrusion detections can be arried out online by the BM pattern match algorithm. Finally all modules of the NIDS are escribed by formalized language.
Key words：Outliers mining；Density；Intrusion detection