By Gilbert Peterson, Sujeet Shenoi
Digital forensics offers with the purchase, renovation, exam, research and presentation of digital proof. Networked computing, instant communications and transportable digital units have accelerated the function of electronic forensics past conventional laptop crime investigations. virtually each crime now includes a few element of electronic proof; electronic forensics presents the options and instruments to articulate this facts. electronic forensics additionally has myriad intelligence purposes. additionally, it has a necessary position in details insurance -- investigations of protection breaches yield worthy info that may be used to layout safer platforms. Advances in electronic Forensics X describes unique study effects and cutting edge purposes within the self-discipline of electronic forensics. additionally, it highlights a few of the significant technical and criminal matters regarding electronic proof and digital crime investigations. The parts of insurance comprise: - net Crime Investigations; - Forensic options; - cellular machine Forensics; - Forensic instruments and coaching. This ebook is the tenth quantity within the annual sequence produced by way of the foreign Federation for info Processing (IFIP) operating workforce 11.9 on electronic Forensics, a global neighborhood of scientists, engineers and practitioners devoted to advancing the cutting-edge of study and perform in electronic forensics. The ebook encompasses a number 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 crucial source for researchers, school individuals and graduate scholars, in addition to for practitioners and participants engaged in study and improvement efforts for the legislation enforcement and intelligence communities.
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Extra resources for Advances in Digital Forensics X: 10th IFIP WG 11.9 International Conference, Vienna, Austria, January 8-10, 2014, Revised Selected Papers
Majumdar, Credit card fraud detection: A fusion approach using Dempster-Shafer theory and Bayesian learning, Information Fusion, vol. 10(4), pp. 354–363, 2009.  K. Sherly and R. Nedunchezhian, BOAT adaptive credit card fraud detection system, Proceedings of the IEEE International Conference on Computational Intelligence and Computing Research, 2010.  S. Viaene, R. Derrig and G. Dedene, A case study of applying boosting naive Bayes to claim fraud diagnosis, IEEE Transactions on Knowledge and Data Engineering, vol.
Raines, M. Grimaila, R. Baldwin, R. Bennington and C. Reuter, The use of attack and protection trees to analyze security for an online banking system, Proceedings of the Fortieth Annual Hawaii International Conference on System Sciences, p. 144b, 2007. id=223), 2011.  B. Kordy, S. Mauw, S. Radomirovic and P. Schweitzer, Attackdefense trees, Journal of Logic and Computation, 2012.  S. Murdoch and R. Anderson, Veriﬁed by Visa and MasterCard SecureCode: Or, how not to design authentication, in Financial Cryptography and Data Security, Springer-Verlag, R.
Distance. The Euclidean distance is another measure for comparing two vectors. This distance is more appropriate when the lengths of the documents are considered. For example, the Euclidean distance measure can be used to compute the nearest neighbors or, in our case, to determine the centroid of the cluster during the document selection process. 4 Feature Selection Feature selection involves the selection of a subset of terms from the training set that is used for the vector space model. This process decreases the cardinality of the vector space and reduces the computation time.
Advances in Digital Forensics X: 10th IFIP WG 11.9 International Conference, Vienna, Austria, January 8-10, 2014, Revised Selected Papers by Gilbert Peterson, Sujeet Shenoi