C4.5: programs for machine learning
C4.5: programs for machine learning
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
PeerReview: practical accountability for distributed systems
Proceedings of twenty-first ACM SIGOPS symposium on Operating systems principles
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Automating Disk Forensic Processing with SleuthKit, XML and Python
SADFE '09 Proceedings of the 2009 Fourth International IEEE Workshop on Systematic Approaches to Digital Forensic Engineering
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
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This paper presents a novel solution to the problem of determining the ownership of carved information found on disk drives and other storage media that have been used by more than one person. When a computer is subject to forensic examination, information may be found that cannot be readily ascribed to a specific user. Such information is typically not located in a specific file or directory, but is found through file carving, which recovers data from unallocated disk sectors. Because the data is carved, it does not have associated file system metadata, and its owner cannot be readily ascertained. The technique presented in this paper starts by automatically recovering both file system metadata as well as extended metadata embedded in files (for instance, embedded timestamps) directly from a disk image. This metadata is then used to find exemplars and to create a machine learning classifier that can be used to ascertain the likely owner of the carved data. The resulting classifier is well suited for use in a legal setting since the accuracy can be easily verified using cross-validation. Our technique also results in a classifier that is easily validated by manual inspection. We report results of the technique applied to both specific hard drive data created in our laboratory and multiuser drives that we acquired on the secondary market. We also present a tool set that automatically creates the classifier and performs validation.