Browsing by Author "Almehmadi, Abdulaziz"

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On the potential of intent-based access control (IBAC) in preventing insider threats
(2015-11-01) Almehmadi, Abdulaziz; El-Khatib, Khalil
Existing 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 thesis, 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.
An unmanned aerial vehicle-based assessment method for quantifying computer vision models
(2019-04-01) Hills, Zachary; El-Khatib, Khalil; Pazzi, Richard; Almehmadi, Abdulaziz
Computer vision is a growing field in computer science. Since the advancement of Machine learning, Computer vision solutions have been trending. As a result of the growing number of solutions and performance increases in Machine learning, machine learning solutions are now being utilized in the field of robotics. A problem propagates when the evaluation methods that were used previously are used for robotic vision solutions. The accuracy metric although valuable from a data driven perspective lends no benefit to the use in robotics. The accuracy calculated by the performance of the Convolutional neural network on the evaluation dataset is only a relevant metric to the evaluation dataset. The accuracy metric does not define the distance at which the accuracy of the Convolutional Neural Network (CNN) begins to decrease below the required threshold. The accuracy metric does not depict the strengths and weaknesses of the CNN in terms of orientation of the object. The accuracy metric does not show the accuracy of the CNN given a specific orientation and distance. Orientation and distance are factors when considering a computer vision solution for the use in robotics. A popular example is Tesla. Tesla incorporates a multitude of systems in order to produce their self-driving capabilities. One of the systems used is camera feed that utilizes Machine learning to depict the context of the image. Tesla needs their system to perform in a multitude of distance and orientation of objects [9]. Simply using a single accuracy metric will not be enough to define the limitations of the system. What this thesis proposes is an evaluative method capable of defining the spatial limitations of a CNN for 3D objects. This approach utilizes Unmanned aerial vehicle (UAV) mobile sensors in order to generate the desired distances and orientation from the object being evaluated. Multiple flight sequences are conducted to provide information that is able to define the exact point in which the accuracy starts to decrease and the orientations that are the most weak. This approach was tested using a two class CNN that depicted if a Ford Ranger was in the image or if it was not. The experimental results using an Unmanned Aerial Vehicle (UAV) was able to depict the CNN's dependencies such as: the distance from the object, the altitude, the orientation of the object and the impact these dependencies have on accuracy. An UAV was used due to their innate capability as mobile sensors capable of producing any perspective and distance required.