基于多传感器数据融合的入侵检测机制

针对特征复杂的入侵方式,设计了一种基于数据融合理论的新型入侵检测机制-DFIDS,结果提高了系统在检测复杂入侵行为时的确定性。DFIDS使用优化的并行分布式检测与决策融合系统模型,可以有效克服传统入侵检测系统因单检测器而在数据采集和分析方面的局限性,从而提高了检测的总体性能。文中建立了DFIDS的理论分析模型,并和传统入侵检测机制进行了对比,结果表明DFIDS在检测准确性方面具有更好的性能。

A Novel IDS Mechanism Based by Data Fusion with Multiple Sensors

This paper presents a novel IDS mechanism based on the theory of data fusion-DFIDS, which is designed, according to intrusion patterns with complex features, to heighten the accuracy of the system while detecting complex intrusion acts. The performance of the whole detecting system is enhanced because DFYDS effectively overcomes the limitations of conventional single sensor detecting system regarding aspects of data collecting and analyzing by using optimized model of the fusion of parallel-distributed detection and decision. In this paper, a theoretical model of DFIDS is established and compared with traditional IDS, which proves that DFIDS is of better performance as to detecting accuracy.