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Use of ontologies and probabilistic relational models to aid in cyber crime investigation decision support.

机译:使用本体和概率关系模型来协助网络犯罪调查决策支持。

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The purpose of this dissertation is to describe a decision support methodology that may be used to determine if there is sufficient information to demonstrate probable cause and validate the completeness of the evidence obtained. This methodology would become the foundation of a framework where information about a crime that was committed may be shared amongst investigators from various law enforcement agencies and industry, prosecutors and other litigators, and analysts to track digital evidence of crimes or, through trend analysis, identify when investigative resources need to be reallocated.; Within the primary purpose of this dissertation, there are two goals. The first goal is to describe several laws that describe the criminal use of computers using an ontology-modeling tool. Two of the crimes modeled will have similar elements of proof while the third ontology model will describe a very different law. The second goal is to postulate a probabilistic model that will be applied to evidence that may be described using the attributes identified within the ontologies. The probabilistic model will be applied against the evidence. Using a "best fit" inference methodology, the model results should identify whether a crime has been committed, and which crime has been committed. The degree of fit will identify if there is sufficient evidence to justify "probable cause" for a search warrant. The three crimes were chosen to see if the models have sufficient granularity to identify which crime has been committed. By identifying the correct crime, this same model will identify which law enforcement agency is responsible for investigating that crime since the enforcement of specific laws (i.e. the investigation of the corresponding crimes) are assigned to specific law enforcement agencies. Future research would allow modification of the probabilistic model to assist investigators in determining if there is sufficient evidence for prosecution. The ontologies need to be sufficiently robust as to allow the description of statutes of crimes that are similar in nature but fall into a different jurisdiction. In this way, the probabilistic model can be used to identify whether an investigation needs to be continued or passed on to another jurisdiction's law enforcement agency for continued investigation, thereby freeing the initial agency's assets for use elsewhere. (Abstract shortened by UMI.)
机译:本文的目的是描述一种决策支持方法,该方法可用于确定是否有足够的信息来证明可能的原因并验证所获得证据的完整性。这种方法将成为框架的基础,在该框架中,可以在来自各种执法机构和行业的调查人员,检察官和其他诉讼人以及分析人员之间共享有关犯罪的信息,以跟踪犯罪的数字证据,或者通过趋势分析来确定需要重新分配调查资源时;在本论文的主要目的内,有两个目标。第一个目标是描述一些法律,这些法律使用本体建模工具来描述计算机的犯罪使用。被建模的两种犯罪将具有相似的证据元素,而第三种本体模型将描述非常不同的法律。第二个目标是假设一个概率模型,该概率模型将应用于可能使用本体中标识的属性来描述的证据。概率模型将被用于证据。使用“最佳匹配”推论方法,模型结果应确定是否已犯罪,以及已犯罪。合适程度将确定是否有足够的证据证明搜查令的“可能原因”是合理的。选择了这三种犯罪,以查看模型是否具有足够的粒度来识别已实施的犯罪。通过确定正确的犯罪,由于将特定法律的执行(即,对相应犯罪的调查)分配给了特定的执法机构,因此该模型将确定哪个执法机构负责调查该犯罪。未来的研究将允许修改概率模型,以帮助调查人员确定是否有足够的证据提出起诉。本体必须足够健壮,以允许描述性质相似但属于不同管辖范围的犯罪法规。通过这种方式,概率模型可用于确定是需要继续进行调查还是将调查移交给另一个辖区的执法机构以进行继续调查,从而释放初始机构的资产以供其他地方使用。 (摘要由UMI缩短。)

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