Download Advances in Digital Forensics X: 10th IFIP WG 11.9 by Gilbert Peterson, Sujeet Shenoi PDF

By Gilbert Peterson, Sujeet Shenoi

Digital forensics bargains with the purchase, upkeep, exam, research and presentation of digital facts. Networked computing, instant communications and transportable digital units have improved the function of electronic forensics past conventional machine crime investigations. essentially each crime now comprises a few element of electronic proof; electronic forensics presents the innovations and instruments to articulate this facts. electronic forensics additionally has myriad intelligence functions. in addition, it has a necessary position in info coverage -- investigations of safety breaches yield necessary info that may be used to layout safer platforms. Advances in electronic Forensics X describes unique examine effects and cutting edge functions within the self-discipline of electronic forensics. additionally, it highlights a few of the significant technical and criminal matters relating to electronic facts and digital crime investigations. The components of assurance comprise: - web Crime Investigations; - Forensic innovations; - cellular gadget Forensics; - Forensic instruments and coaching. This publication is the tenth quantity within the annual sequence produced by way of the foreign Federation for info Processing (IFIP) operating crew 11.9 on electronic Forensics, a world group of scientists, engineers and practitioners devoted to advancing the cutting-edge of analysis and perform in electronic forensics. The booklet includes a choice of twenty-two edited papers from the tenth Annual IFIP WG 11.9 overseas convention on electronic Forensics, held in Vienna, Austria within the wintry weather of 2014. Advances in electronic Forensics X is a vital source for researchers, college individuals and graduate scholars, in addition to for practitioners and participants engaged in learn and improvement efforts for the legislations enforcement and intelligence communities.

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Read or Download Advances in Digital Forensics X: 10th IFIP WG 11.9 International Conference, Vienna, Austria, January 8-10, 2014, Revised Selected Papers PDF

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Additional info for Advances in Digital Forensics X: 10th IFIP WG 11.9 International Conference, Vienna, Austria, January 8-10, 2014, Revised Selected Papers

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Figure 4. 3 Combination Similarity (d) Classification F1 -measure. Classification performance of document selection IRC channels. While single-link clustering reduces the number of clusters for the given similarity values more rapidly, it produces a significant accuracy loss. In contrast, complete-link clustering can reduce the number of needed training samples to less than 40% with minimal loss of recall. As shown in Figure 4(d), the selection methods for the cluster representative, which would be added to the training set, performed equally well for the upper interval of the combination similarity.

Aleskerov, B. Freisleben and B. Rao, Cardwatch: A neural network based database mining system for credit card fraud detection, Proceedings of the IEEE/IAFE Conference on Computational Intelligence for Financial Engineering, pp. 220–226, 1997. [2] S. Bhattacharyya, J. Sanjeev, K. Tharakunnel and J. Westland, Data mining for credit card fraud: A comparative study, Decision Support Systems, vol. 50(3), pp. 602–613, 2011. [3] R. Chen, S. Luo, X. Liang and V. Lee, Personalized approach based on SVM and ANN for detecting credit card fraud, Proceedings of the International Conference on Neural Networks and the Brain, vol.

In the classification process, a “document” is assumed to be an IRC chatroom or a web forum (thread), and “terms” are the words in IRC messages or web forum posts. The terms are mapped from each document to a numeric vector via the bag of words (BoW) model [4]. This model is agnostic to the exact ordering of terms within a document and interprets the terms as a set for each document. The resulting vector space model allows different weightings of the frequencies of individual terms. 1 Text Preprocessing The raw training data contained noise and content that was not relevant to classification.

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