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    基于攻击特征的自动证据筛选技术

    Filtering Intrusion Forensic Data Based on Attack Signatures

    • 摘要: 为了自动获得入侵证据,提出一种基于攻击特征的自动证据筛选方法.其原理是首先根据被调查攻击的特征重构出攻击行为细节,并从中抽取筛选证据需要的“特征信息”.然后,再根据候选数据与这些特征信息的匹配程度筛选出该攻击相关的证据.基于DARPA 2000的实验表明这种方法具有很高的准确率,其完备性更是接近100%.而与现有方法的比较则显示出这种方法能克服现有方法人工干预较多、效率低下、仅能筛选特定证据类型、不适合处理复杂攻击等诸多缺陷.

       

      Abstract: Computer forensics is a new field on computer evidences process. This field is very important and practical, so it has drawn more and more attention in recent years. Intrusion forensics is a specific area of computer forensics, and has been applied to computer intrusion activities. It is a hot area because a large proportion of the computer crimes are intrusion activities. When investigating intrusion activities, one key step is obtaining intrusion evidences. In order to get this kind of evidences automatically, an attack-signature-based method for filtering intrusion forensic data is proposed. It mainly includes the following steps: Firstly, the detail behaviors of the attack being investigated are reconstructed based on its attack signatures; Then the attack features which are required by the filter are extracted from these details; Finally, according to the similarity between attack features and candidate data, all evidences related to the attack being investigated can be gotobtained. The experiment results on DARPA 2000 have proved that our method has high accuracy and its completeness is almost 100%. Compared with current methods, our method shows more advantages. For example it needs little manual work and can process more complex intrusion scenarios. Moreover, it has higher performance and can find more types of evidences.

       

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