Automated Data Type Identification and Localization Using Statistical Analysis Data Identification

This research presents a new and unique technique called SÁDI, statistical analysis data identification, for identifying the type of data on a digital device and its storage format based on data type, specifically the values of the bytes representing the data being examined. This research incorporates the automation required for specialized data identification tools to be useful and applicable in real-world applications. The SÁDI technique utilizes the byte values of the data stored on a digital storage device in such a way that the accuracy of the technique does not rely solely on the potentially misleading metadata information but rather on the values of the data itself. SÁDI provides the capability to identify what digitally stored data actually represents. The identification of the relevancy of data is often dependent upon the identification of the type of data being examined. Typical file type identification is based upon file extensions or magic keys. These typical techniques fail in many typical forensic analysis scenarios, such as needing to deal with embedded data, as in the case of Microsoft Word files or file fragments. These typical techniques for file identification can also be easily circumvented, and individuals with nefarious purposes often do so

Approaches to the classification of high entropy file fragments.

In this paper we propose novel approaches to the problem of classifying high entropy file fragments. We achieve 97% correct classification for encrypted fragments and 78% for compressed. Although classification of file fragments is central to the science of Digital Forensics, high entropy types have been regarded as a problem. Roussev and Garfinkel [1] argue that existing methods will not work on high entropy fragments because they have no discernible patterns to exploit. We propose two methods that do not rely on such patterns. The NIST statistical test suite is used to detect randomness in 4KB fragments. These test results were analysed using Support Vector Machines, k-Nearest-Neighbour analysis and Artificial Neural Networks (ANN). We compare the performance of each of these analysis methods. Optimum results were obtained using an ANN for analysis giving 94% and 74% correct classification rates for encrypted and compressed fragments respectively. We also use the compressibility of a fragment as a measure of its randomness. Correct classification was 76% and 70% for encrypted and compressed fragments respectively. Although it gave poorer results for encrypted fragments we believe that this method has more potential for future work. We have used subsets of the publicly available GovDocs1 Million File Corpus‘ so that any future research may make valid comparisons with the results obtained here

Persevering with Great Abandon: An Archaeobotanical Investigation of Resilience and Sustainability in Eastern African Irrigated Terrace Agriculture

The site of Engaruka in northern Tanzania has been the focus of decades of archaeological research regarding the development of terraced field systems in East Africa. Engaruka is a vast agricultural landscape, occupied from the 14th century and abandoned in 18th century. The abandonment of such a large and intensively cultivated area has been interpreted by some policy makers as a response to a failure of the agronomy, as has been argued elsewhere. This PhD research represents the archaeobotanical component of the AAREA (Archaeology of Agricultural Resilience in East Africa) Project, which was focused on establishing the efficacy of applying archaeological results that are in a dynamic state of development to policy decisions regarding agricultural resilience and sustainability. This study focuses on the identification of crops in cultivation at Engaruka during its occupation based on the analysis of archaeobotanical residues (e.g. charred plant remains), as well as historic and ethnographic observations of cultivation throughout the region. The results confirm the presence of sorghum and other millets as well as several pulses, disproving the argument that ancient Engarukans were practicing sorghum monoculture. These data have been queried to address questions about the presence and preservation of millets and pulses and non-crop taxa in both expected and unlikely contexts, providing information on a range of issues including cultivation strategy and practice, specifically relating to harvesting techniques, the role of wild and weedy taxa, and differential use of space. Discussion is based upon detailed investigations of plant cultivation, collection/harvest, and exploitation through quantification of charred plant macrofossils, gathered weeds/wild taxa, and interview data relating to farming practices, thus highlighting the strengths of a multi-disciplinary approach for understanding resilience, sustainability, and, more generally, what it means to subsist in a challenging and dynamic agricultural landscape

Previsão agricola : uma nova abordagem : uso de scanner aerotransportavel e redes neurais

Tese (doutorado) - Universidade Federal de Santa Catarina, Centro TecnologicoO objetivo principal deste trabalho foi de avançar no entendimento de modelos de previsão de fenofases de cultura perene (maçã), rendimentos e safras. Foram desenvolvidos, testados e comparados modelos de previsão polinomiais contra redes neurais artificiais. A combinação destes modelos foi testada, via programação linear, buscando minimizar erros relativos das previsões. Paralelamente, desenvolveu-se metodologia de análise espacial para caracterização de propriedade agrícola com o intuito de discriminação de áreas cultivadas, em nível de espécie e cultivares. Para tanto, foram utilizadas imagens digitais obtidas por scanner aerotransportável (CASI) e de aerofotos de vôo fotogramétrico. A área de estudo compreendeu 920ha, Fraiburgo/SC. Sistemas de informações geográficas - GIS foi empregado para integração e manipulação de dados. Os modelos polinomiais e neurais tiveram desempenho previsivo semelhante. As imagens CASI permitiram a discriminação de 4 cultivares de maçã, em plantios comerciais com menos de 1.000 plantas/ha, sistema de condução consorciados e pequenas áreas (< 3ha)