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Product Identifiers
PublisherCreateSpace
ISBN-101983529184
ISBN-139781983529184
eBay Product ID (ePID)28038685564
Product Key Features
Number of Pages212 Pages
Publication NameInsider Threats Meet Access Control : Insider Threats Detected Using Intent-Based Access Control (IBAC)
LanguageEnglish
SubjectComputer Science
Publication Year2018
TypeTextbook
AuthorAbdulaziz Almehmadi
Subject AreaComputers
FormatTrade Paperback
Dimensions
Item Height0.5 in
Item Weight13.5 Oz
Item Length9 in
Item Width6 in
Additional Product Features
Intended AudienceTrade
SynopsisExisting access control mechanisms are based on the concepts of identity enrollment and recognition, and assume that recognized identity is synonymous with ethical actions. However, statistics over the years show that the most severe security breaches have been the results of trusted, authorized, and identified users who turned into malicious insiders. Therefore, demand exists for designing prevention mechanisms. A non-identity-based authentication measure that is based on the intent of the access request might serve that demand.In this book, we test the possibility of detecting intention of access using involuntary electroencephalogram (EEG) reactions to visual stimuli. This method takes advantage of the robustness of the Concealed Information Test to detect intentions. Next, we test the possibility of detecting motivation of access, as motivation level corresponds directly to the likelihood of intent execution level. Subsequently, we propose and design Intent-based Access Control (IBAC), a non-identity-based access control system that assesses the risk associated with the detected intentions and motivation levels. We then study the potential of IBAC in denying access to authorized individuals who have malicious plans to commit maleficent acts. Based on the access risk and the accepted threshold established by the asset owners, the system decides whether to grant or deny access requests.We assessed the intent detection component of the IBAC system using experiments on 30 participants and achieved accuracy of 100% using Nearest Neighbor and SVM classifiers. Further, we assessed the motivation detection component of the IBAC system. Results show different levels of motivation between hesitation-based vs. motivation-based intentions. Finally, the potential of IBAC in preventing insider threats by calculating the risk of access using intentions and motivation levels as per the experiments shows access risk that is different between unmotivated and motivated groups. These results demonstrate the potential of IBAC in detecting and preventing malicious insiders.
