{"title":"从数字取证报告到贝叶斯网络表示","authors":"Robert Lee, S. Lang, Kevin Stenger","doi":"10.1109/ISI.2009.5137330","DOIUrl":null,"url":null,"abstract":"Computer (digital) forensic examiners typically write a report to document the examination process, including tools used, major processing steps, summary of the findings, and a detailed listing of relevant evidence (files, artifacts) exported to external media (CD, DVD, hard copy) for the case investigator or attorney. However, proper interpretation of the significance of extracted evidence often requires additional consultation with the examiner. This paper proposes a practical methodology for transforming the findings in typical forensic reports to a graphical representation using Bayesian networks (BNs). BNs offer the following advantages: (1) Delineate the cause-effect relationship among relevant pieces of evidence described in the report; and (2) Use probability and established Bayesian inference rules to deal with uncertainty of digital evidence. A realistic forensic report is used to demonstrate this methodology.","PeriodicalId":210911,"journal":{"name":"2009 IEEE International Conference on Intelligence and Security Informatics","volume":"38 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"From digital forensic report to Bayesian network representation\",\"authors\":\"Robert Lee, S. Lang, Kevin Stenger\",\"doi\":\"10.1109/ISI.2009.5137330\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Computer (digital) forensic examiners typically write a report to document the examination process, including tools used, major processing steps, summary of the findings, and a detailed listing of relevant evidence (files, artifacts) exported to external media (CD, DVD, hard copy) for the case investigator or attorney. However, proper interpretation of the significance of extracted evidence often requires additional consultation with the examiner. This paper proposes a practical methodology for transforming the findings in typical forensic reports to a graphical representation using Bayesian networks (BNs). BNs offer the following advantages: (1) Delineate the cause-effect relationship among relevant pieces of evidence described in the report; and (2) Use probability and established Bayesian inference rules to deal with uncertainty of digital evidence. A realistic forensic report is used to demonstrate this methodology.\",\"PeriodicalId\":210911,\"journal\":{\"name\":\"2009 IEEE International Conference on Intelligence and Security Informatics\",\"volume\":\"38 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2009-06-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2009 IEEE International Conference on Intelligence and Security Informatics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISI.2009.5137330\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 IEEE International Conference on Intelligence and Security Informatics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISI.2009.5137330","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
From digital forensic report to Bayesian network representation
Computer (digital) forensic examiners typically write a report to document the examination process, including tools used, major processing steps, summary of the findings, and a detailed listing of relevant evidence (files, artifacts) exported to external media (CD, DVD, hard copy) for the case investigator or attorney. However, proper interpretation of the significance of extracted evidence often requires additional consultation with the examiner. This paper proposes a practical methodology for transforming the findings in typical forensic reports to a graphical representation using Bayesian networks (BNs). BNs offer the following advantages: (1) Delineate the cause-effect relationship among relevant pieces of evidence described in the report; and (2) Use probability and established Bayesian inference rules to deal with uncertainty of digital evidence. A realistic forensic report is used to demonstrate this methodology.