Cybersecurity Awareness Framework for Academia
Authors : Mohammed Khader ,Marcel Karam , Hanna Fares
Abstract : Cybersecurity is a multifaceted global phenomenon representing complex socio-technical challenges for governments and private sectors. With technology constantly evolving, the types and numbers of cyberattacks affect different users in different ways. The majority of recorded cyberattacks can be traced to human errors. Despite being both knowledge- and environment-dependent, studies show that increasing users’ cybersecurity awareness is found to be one of the most effective protective approaches. However, the intangible nature, socio-technical dependencies, constant technological evolutions, and ambiguous impact make it challenging to offer comprehensive strategies for better communicating and combatting cyberattacks. Research in the industrial sector focused on creating institutional proprietary risk-aware cultures. In contrast, in academia, where cybersecurity awareness should be at the core of an academic institution’s mission to ensure all graduates are equipped with the skills to combat cyberattacks, most of the research focused on understanding students’ attitudes and behaviors after infusing cybersecurity awareness topics into some courses in a program. This work proposes a conceptual Cybersecurity Awareness Framework to guide the implementation of systems to improve the cybersecurity awareness of graduates in any academic institution. This framework comprises constituents designed to continuously improve the development, integration, delivery, and assessment of cybersecurity knowledge into the curriculum of a university across different disciplines and majors; this framework would thus lead to a better awareness among all university graduates, the future workforce. This framework may be adjusted to serve as a blueprint that, once adjusted by academic institutions to accommodate their missions, guides institutions in developing or amending their policies and procedures for the design and assessment of cybersecurity awareness
Keywords : cybersecurity, awareness, curriculum, computer science, information technology, education, framework, courses, content design
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A Multicomponent Approach to Nonrigid Registration of Diffusion Tensor Images
Authors : Mohammed Khader, Emanuele Schiavi , A. Ben Hamza
Abstract : Diffusion tensor imaging has shown promise in the early detection and diagnosis of a host of disorders and neurologic conditions. In this paper, we propose a nonrigid registration approach for diffusion tensor images using a multicomponent information-theoretic measure. Explicit orientation optimization is enabled by incorporating tensor reorientation, which is necessary for wrapping diffusion tensor images. Experimental results on diffusion tensor images indicate the feasibility of the proposed approach and a much better performance compared to the affine registration method based on mutual information in terms of registration accuracy in the presence of geometric distortion.
Keywords : Diffusion tensor imaging, Image registration, Nonrigid, Tsallis entropy
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An information-theoretic method for multimodality medical image registration
Authors : Mohammed Khader , A. Ben Hamza
Abstract : In this paper, an information-theoretic approach for multimodal image registration is presented. In the proposed approach, image registration is carried out by maximizing a Tsallis entropy-based divergence using a modified simultaneous perturbation stochastic approximation algorithm. This divergence measure achieves its maximum value when the conditional intensity probabilities of the transformed target image given the reference image are degenerate distributions. Experimental results are provided to demonstrate the registration accuracy of the proposed approach in comparison to existing entropic image alignment techniques. The feasibility of the proposed algorithm is demonstrated on medical images from magnetic resonance imaging, computer tomography, and positron emission tomography.
Keywords : Image registration, Tsallis entropy, Stochastic optimization
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Nonrigid image registration using an entropic similarity
Authors : Mohammed Khader , A. Ben Hamza
Abstract : In this paper, we propose a nonrigid image registration technique by optimizing a generalized information-theoretic similarity measure using the quasi-Newton method as an optimization scheme and cubic B-splines for modeling the nonrigid deformation field between the fixed and moving 3-D image pairs. To achieve a compromise between the nonrigid registration accuracy and the associated computational cost, we implement a three-level hierarchical multiresolution approach such that the image resolution is increased in a coarse to fine fashion. Experimental results are provided to demonstrate the registration accuracy of our approach. The feasibility of the proposed method is demonstrated on a 3-D magnetic resonance data volume and also on clinically acquired 4-D CT image datasets.
Keywords : Entropy, Spline, Image registration, Kernel, Accuracy, Deformable models, Biomedical measurements
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Entropic Framework for Nonrigid Registration of Diffusion Tensor Images
Authors : Mohammed Khader , A. Ben Hamza
Abstract : We propose a nonrigid registration approach for diffusion tensor images using a multicomponent information-theoretic measure. Explicit orientation optimization is enabled by incorporating tensor reorientation. Experimental results on medical images indicate the feasibility of the proposed approach not only in terms of registration accuracy in the presence of geometric distortion but also in terms of robustness to noise.
Keywords : Tensile stress, Diffusion tensor imaging, Biomedical imaging, Accuracy, Image registration, Anisotropic magnetoresistance, Optimization
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An Entropy-Based Technique for Nonrigid Medical Image Alignment
Authors : Mohammed Khader , A. Ben Hamza
Abstract : In this paper, we propose a nonrigid image registration technique by minimizing an information-theoretic measure using the quasi-Newton method as an optimization scheme and a cubic B-spline for modeling the nonrigid deformation field between the reference and target 3D image pairs. Experimental results are provided to demonstrate the registration accuracy of the proposed approach. The feasibility of our method is demonstrated on a 3D magnetic resonance data volume.
Keywords : Image registration, entropy, nonrigid
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Multimodality Image Alignment Using Information-Theoretic Approach
Authors : Mohammed Khader, A. Ben Hamza, Prabir Bhattacharya
Abstract : In this paper, an entropic approach for multimodal image registration is presented. In the proposed approach, image registration is carried out by maximizing a Tsallis entopy-based divergence using a modified simultaneous perturbation stochastic approximation algorithm. This divergence measure achieves its maximum value when the conditional intensity probabilities of the transformed target image given the reference image are degenerate distributions. Experimental results are provided to demonstrate the registration accuracy of the proposed approach in comparison to existing entropic image alignment techniques. The feasibility of the proposed algorithm is demonstrated on medical images from magnetic resonance imaging, computer tomography, and positron emission tomography.
Keywords : Image registration, Tsallis entropy, stochastic optimization
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A New Steganography Algorithm for Text Hiding in the Frequency Domain
Authors : M. Khader, M. B. Al-Zoubi
Abstract : In this paper we develop a new steganography algorithm operating in the frequency domain for grayscale digital images. The algorithm embeds a message by an encoding system in a selected set of DCT coefficients. The message is embedded by modulating the relative size of specific DCT coefficients in the middle frequencies, Before embedding, the mean of each 8×8 block is calculated and compared with DCT coefficients during the embedding process. A decoding algorithm is also presented to extract the embedded message by using the mean of each 8×8 block. Different experiments were carried out to test the performance of the proposed algorithm. The results proved that our algorithm maximizes the payload and yet, the resultant (stego) images are invisible, secure and robust to various attacks. In addition, our algorithm produces high image quality with no visual difference between the original and the stego-image.
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