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作者 Peisert, Sean Philip
書名 A model of forensic analysis using goal-oriented logging
國際標準書號 9780493792958
book jacket
說明 182 p
附註 Source: Dissertation Abstracts International, Volume: 67-12, Section: B, page: 7174
Adviser: Sidney Karin
Thesis (Ph.D.)--University of California, San Diego, 2007
Forensic analysis is the process of understanding, re-creating, and analyzing arbitrary events that have previously occurred. It seeks to answer such questions as how an intrusion occurred, what an attacker did during an intrusion, and what the effects of an attack were
Currently the field of computer forensics is largely ad hoc. Data is generally collected because applications log it for debugging purposes or because someone thought it to be important. Practical forensic analysis has traditionally traded off analyzability against the amount of data recorded. Recording less data puts a smaller burden both on computer systems and on the humans that analyze them. Not recording enough data leaves analysts drawing their conclusions based on inference, rather than deduction
This dissertation presents a model of forensic analysis, called Laocoon, designed to determine what data is necessary to understand past events. The model builds upon an earlier model used for intrusion detection, called the requires/provides model. The model is based on a set of qualities we believe a good forensic model should possess. Those qualities are in turn influenced by a set of five principles of computer forensic analysis. We apply Laocoon to examples, and present the results for a UNIX system. The results demonstrate how the model can be used to record smaller amounts of highly useful data, rather than forcing a choice between overwhelming amounts of data or such a small amount of data to be effectively useless
School code: 0033
DDC
Host Item Dissertation Abstracts International 67-12B
主題 Computer Science
0984
Alt Author University of California, San Diego
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