By Asaf Shabtai
SpringerBriefs current concise summaries of state-of-the-art learn and functional 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 a number content material from expert to educational. Briefs permit authors to provide their rules and readers to soak up them with minimum time funding. As a part of Springer’s book assortment, SpringBriefs are released to thousands of clients all over the world. Information/Data Leakage poses a major risk to businesses and organisations, because the variety of leakage incidents and the associated fee they inflict maintains to extend. no matter if attributable to malicious purpose, or an inadvertent mistake, facts loss can minimize a company’s model, decrease shareholder worth, and harm the company’s goodwill and popularity. This booklet goals to supply a structural and complete evaluate of the sensible recommendations and present examine within the DLP area. this can be the 1st entire ebook that's devoted completely to the sector of information leakage and covers all very important demanding situations and methods to mitigate them. Its informative, actual pages will supply researchers, scholars and practitioners within the with a accomplished, but concise and handy reference resource to this attention-grabbing box. now we have grouped present options into assorted different types according to a defined taxonomy. The offered taxonomy characterizes DLP strategies in line with numerous facets resembling: leakage resource, info nation, leakage channel, deployment scheme, preventive/detective techniques, and the motion upon leakage. within the advertisement half we evaluation strategies of the prime DLP industry avid gamers in accordance with specialist examine experiences and fabric received from the internet sites of the owners. within the educational half we cluster the educational paintings in keeping with the character of the leakage and safeguard into a number of different types. eventually, we describe major information leakage eventualities and current for every situation the main proper and acceptable answer or procedure that might mitigate and decrease the possibility and/or influence of the leakage scenario.
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Extra resources for A Survey of Data Leakage Detection and Prevention Solutions
These studies discuss two kinds of honeypots: honeytokens and honeyfiles. , a database, a file or an email message). , a PowerPoint presentation, an Excel spreadsheet, or an email message). Internal attackers pose a much greater challenge to organizations because they narrow the detection window available for existing countermeasures such as IDS, firewalls, and IPS. Valli (2005) asserted that more stringent assumptions should be made when using honeypots against insider threats, for example: an insider’s legitimate access privileges; existence of high-speed network connections and access to the honeypot; deep acquaintance with the defense configuration and its weaknesses; and knowledge of earlier states of the application architecture, technologies, and functionalities.
Three scenarios were evaluated: short queries used for deriving profiles and for detection; long queries used for deriving profiles and for detection; and long queries used for deriving 26 4 Data Leakage Detection/Prevention Solutions profiles and short queries used for detection. The results demonstrate that for each of the tested scenarios, one or more of the classifiers performs as well as or better than the manual adjustment. Mun et al. (2008) proposed the use of an intrusion detection system for detecting insider attackers.
The content-based approach for detecting and preventing data leakage can be further divided into: • keywords-based rules. In this approach, various rules are retrieved from keywords that appear in the body and the header of an email. 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.