By Asaf Shabtai
SpringerBriefs current concise summaries of state of the art study and useful functions throughout a large spectrum of fields. that includes compact volumes of fifty to a hundred pages (approximately 20,000- 40,000 words), the sequence covers more than a few content material from expert to educational. Briefs permit authors to provide their principles and readers to take in them with minimum time funding. As a part of Springer’s book assortment, SpringBriefs are released to thousands of clients around the globe. Information/Data Leakage poses a major probability to businesses and agencies, because the variety of leakage incidents and the price they inflict maintains to extend. no matter if attributable to malicious reason, or an inadvertent mistake, facts loss can slash a company’s model, decrease shareholder worth, and harm the company’s goodwill and recognition. This e-book goals to supply a structural and finished evaluate of the sensible ideas and present study within the DLP area. this is often the 1st entire e-book that's committed solely to the sector of information leakage and covers all very important demanding situations and strategies to mitigate them. Its informative, genuine pages will supply researchers, scholars and practitioners within the with a finished, but concise and handy reference resource to this attention-grabbing box. now we have grouped present suggestions into varied different types according to a defined taxonomy. The awarded taxonomy characterizes DLP strategies based on a number of points resembling: leakage resource, info country, leakage channel, deployment scheme, preventive/detective methods, and the motion upon leakage. within the advertisement half we assessment strategies of the best DLP marketplace avid gamers in response to specialist study stories and fabric got from the internet sites of the proprietors. within the educational half we cluster the tutorial paintings based on the character of the leakage and defense into quite a few different types. ultimately, we describe major information leakage eventualities and current for every situation the main suitable and acceptable answer or technique that may mitigate and decrease the possibility and/or influence of the leakage scenario.
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Extra info for A Survey of Data Leakage Detection and Prevention Solutions
Therefore, solutions which prevent the data from illegally leaving the controlled systems should be in place. In some cases, the data are legally exported (for example, to generate a report containing statistical information). If exporting the data is legal and should not be prevented, the data might still leak out, for example, if the report were sent to a wrong recipient with an email message or stored on a USB stick which is lost. However, such cases are beyond the scope of this scenario and have been addressed by scenarios mentioned earlier.
An outlier detection algorithm identifies user behavior which deviates from the profiles. Each query is represented by extracting features from the query syntax. The extracted features refer to SQL commands, accessed relational information, and accessed attribute information. 2 Academic research in the DLP domain 27 accessed by the query); and fine-grained (the most detailed, explicitly lists which attributes in which relation were accessed). The evaluation of the proposed method was conducted using a naïve Bayes classifier and a real dataset consisting of 7,583 SELECT commands, 213 INSERT commands, and 572 UPDATE commands and anomalous queries that were generated and injected into the datasets.
These rules determine the “confidentiality level” of the scanned email based on the number of appearances of certain keywords [Cohen, 1996], [Helfman, 1995] and [Rennie, 2000]. • machine learning techniques. The basic idea of this approach is to use machine learning techniques such as SVM ([Cohen, 1999], [Drucker, 1999]) and naïve Bayes ([Androutsopoulos, 2000], [Hovold, 2005], and [Sahami, 1998]) to determine the “confidentiality level” of the scanned email message. 2 Academic research in the DLP domain 29 Two methods are used to represent textual data in emails.