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A heuristic for maximizing investigation effectiveness of digital forensic cases involving multiple investigators

机译：最大化涉及多个调查员的数字取证案件调查效率的启发式方法

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Digital forensic investigation refers to the use of science and technology in the process of investigating a crime scene so as to maximize the effectiveness of proving the perpetrator has committed crime in a court of law. Evidences are considered to be the building block of any crime scene investigation (CSI) procedure including those involving cyber crimes. Selecting the right set of evidence and assigning the appropriate investigator for the selected evidence is vital in time critical forensic cases, in which results have to be finalized within a specified time deadline. Not doing this may lead to the scope creep problem, which is a significant issue in digital forensics. Therefore, major challenges with respect to digital forensic investigation are to determine the right set of evidences to be assigned to each of the available multiple investigators and allocate appropriate investigation time for the selected evidences to maximize the effectiveness of the investigation effort. A mixed integer linear programming (MILP) model is developed to analyze and solve the problem of evidence selection and resource allocation in a digital crime scene investigation. In view of the problem being NP-hard, a heuristic algorithm with polynomially bounded computational complexity is proposed to solve the problem. Results of extensive computational experiments to empirically evaluate its effectiveness to find an optimal or near-optimal solution are reported. Finally, this paper concludes with a summary of findings and some fruitful directions for future research. (C) 2015 Elsevier Ltd. All rights reserved.
机译：数字取证调查是指在调查犯罪现场的过程中使用科学技术，以最大程度地证明犯罪者在法庭上犯罪。证据被认为是任何犯罪现场调查（CSI）程序的基础，包括那些涉及网络犯罪的程序。在时间紧迫的法医案件中，选择正确的证据集并为选定的证据指派适当的研究人员至关重要，在这种情况下，必须在指定的时间期限内确定结果。不这样做可能会导致示波器蠕变问题，这是数字取证中的一个重要问题。因此，数字取证调查的主要挑战是确定要分配给可用的多个调查员中的每一个的正确证据集，并为选定的证据分配适当的调查时间，以最大程度地提高调查工作的效率。建立了混合整数线性规划（MILP）模型，以分析和解决数字犯罪现场调查中的证据选择和资源分配问题。鉴于问题是NP难的，提出了一种具有多项式有限计算复杂度的启发式算法来解决该问题。报告了广泛的计算实验的结果，这些实验经验地评估了其找到最佳或接近最佳解决方案的有效性。最后，本文以结论的总结和未来研究的一些有益方向作为结尾。 （C）2015 Elsevier Ltd.保留所有权利。

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