A Novel Data Mining-Based Method for Alert Reduction and Analysis
Current system managers often have to process huge amounts of alerts per day, which may be produced by all kinds of security products, network management tools or system logs. This has made it extremely difficult for managers to analyze and react to threats and attacks. So an effective technique which can automatically filter and analyze alerts has become urgent need. This paper presents a novel method for handling IDS alerts more efficiently. It introduces a new data mining technique, outlier detection, into this field, and designs a special outlier detection algorithm for identifying true alerts and reducing false positives (i.e. alerts that are triggered incorrectly by benign events). This algorithm uses frequent attribute values mined from historical alerts as the features of false positives, and then filters false alerts by the score calculated based on these features. We also proposed a three-phrase framework, which not only can filter newcome alerts in real time, but also can learn from these alerts and automatically adjust the filtering mechanism to new situations. Moreover our method can help managers analyze the root causes of false positives. And our method needs no domain knowledge and little human assistance, so it is more practical than current ways. We have built a prototype implementation of our method. Through the experiments on DARPA 2000, we have proved that our model can effectively reduce false positives. And on real-world dataset, our model has even higher reduction rate. By comparing with other alert reduction methods, we believe that our model has better performance.