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Full-Text Articles in Computer Sciences
Convolutional Neural Networks For Deflate Data Encoding Classification Of High Entropy File Fragments, Nehal Ameen
Convolutional Neural Networks For Deflate Data Encoding Classification Of High Entropy File Fragments, Nehal Ameen
University of New Orleans Theses and Dissertations
Data reconstruction is significantly improved in terms of speed and accuracy by reliable data encoding fragment classification. To date, work on this problem has been successful with file structures of low entropy that contain sparse data, such as large tables or logs. Classifying compressed, encrypted, and random data that exhibit high entropy is an inherently difficult problem that requires more advanced classification approaches. We explore the ability of convolutional neural networks and word embeddings to classify deflate data encoding of high entropy file fragments after establishing ground truth using controlled datasets. Our model is designed to either successfully classify file …
Automated Timeline Anomaly Detection, Joshua M. Barone
Automated Timeline Anomaly Detection, Joshua M. Barone
University of New Orleans Theses and Dissertations
Digital forensics is the practice of trained investigators gathering and analyzing evidence from digital devices such as computers and smart phones. On these digital devices, it is possible to change the time on the device for a purpose other than what is intended. Currently there are no documented techniques to determine when this occurs. This research seeks to prove out a technique for determining when the time has been changed on forensic disk image by analyzing the log files found on the image. Out of this research a tool is created to perform this analysis in automated fashion. This tool …
Android Memory Capture And Applications For Security And Privacy, Joseph T. Sylve
Android Memory Capture And Applications For Security And Privacy, Joseph T. Sylve
University of New Orleans Theses and Dissertations
The Android operating system is quickly becoming the most popular platform for mobiledevices. As Android’s use increases, so does the need for both forensic and privacy toolsdesigned for the platform. This thesis presents the first methodology and toolset for acquiringfull physical memory images from Android devices, a proposed methodology for forensicallysecuring both volatile and non-volatile storage, and details of a vulnerability discovered by theauthor that allows the bypass of the Android security model and enables applications to acquirearbitrary permissions.