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中文摘要：
入侵特征值识别和发现算法是误用入侵检测中的关键技术。针对数据挖掘经典的Apriori算法中多次扫描事务数据库而产生很大I/O负载和可能产生庞大的无用候选集的问题,提出了一种基于快速多规则约束Apriori算法。算法实时更新了入侵检测系统的规则库,提高了整个系统的检测性能,有效降低虚警率和误报率。同时考虑到强规则事件并不一定是有趣事件的问题,算法加入递减支持度约束。试验结果显示,该算法相比Apriori算法在系统的入侵检测效率上有很好的改善。
英文摘要：
Invasion eigenvalue and discovery algorithm are the key technologies to misuse intrusion detection technology.To solve Apriori algorithm＇s two problems： one is that scanning the transaction database repeatedly produce large I/O load;the other is that it may have unwanted large candidate sets.Presents a fast multi-constrained Apriori algorithm,which can real-time update of intrusion detection system rule-base,improve detection performance of the entire system,effectively reduce the false alarm rate and false alarm rate.Considering that the current new attacks are derivatives of old existing attacks,many of them have same characteristics of sub-strings,and not every events with strong rules are fun events,the new algorithm adds the decreasing support constraints.Experiment results indicate that the proposed method is efficient.
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