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Biomedical Optics Express (21567085)13(9)pp. 4539-4558
Optical coherence tomography (OCT) is an emerging imaging technique for ophthalmic disease diagnosis. Two major problems in OCT image analysis are image enhancement and image segmentation. Deep learning methods have achieved excellent performance in image analysis. However, most of the deep learning-based image analysis models are supervised learning-based approaches and need a high volume of training data (e.g., reference clean images for image enhancement and accurate annotated images for segmentation). Moreover, acquiring reference clean images for OCT image enhancement and accurate annotation of the high volume of OCT images for segmentation is hard. So, it is difficult to extend these deep learning methods to the OCT image analysis. We propose an unsupervised learning-based approach for OCT image enhancement and abnormality segmentation, where the model can be trained without reference images. The image is reconstructed by Restricted Boltzmann Machine (RBM) by defining a target function and minimizing it. For OCT image enhancement, each image is independently learned by the RBM network and is eventually reconstructed. In the reconstruction phase, we use the ReLu function instead of the Sigmoid function. Reconstruction of images given by the RBM network leads to improved image contrast in comparison to other competitive methods in terms of contrast to noise ratio (CNR). For anomaly detection, hyper-reflective foci (HF) as one of the first signs in retinal OCTs of patients with diabetic macular edema (DME) are identified based on image reconstruction by RBM and post-processing by removing the HFs candidates outside the area between the first and the last retinal layers. Our anomaly detection method achieves a high ability to detect abnormalities. © 2022 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement
Information Systems Frontiers (15729419)22(1)pp. 187-201
Information security investment is of high importance in management of IT infrastructure. There are many researches focused on game theoretical modeling and analysis of security investment of interdependent firms against potential security attacks. However, these studies usually are not concerned with dynamic and strategic nature of attacks which are increasingly important features of today’s cyber systems. Strategic attackers are those who are able to substitute their investments among targets over time by shifting investments towards poorly protected targets in order to obtain more potential financial gains. In this paper we try to analyze the effects of interdependency in security investment of firms against strategic attackers. Note that although there are a limited number of works that consider the strategic nature of attack, they model the defenders as a set of isolated nodes. Hence the positive externality caused by interconnection of the firms is not considered in these models. We consider both the attackers’ actual strategic behaviors (that causes negative externality via the possibility of substituting the target) as well as structural effects of the networked firms (that leads to positive externality via attack propagation). We propose a differential game among the networked firms in which attackers act strategically. In the proposed game, by employing a linear substitution model for characterizing the process of target selection by the attacker, the open-loop Nash solutions are highlighted in an analytical form. The analytical results show how interconnectivity between firms and the strategic behavior of the attacker determines the firms’ incentives for security investment. It is shown that overinvestment or underinvestment could occur depending on the degree of interdependency among the given firms. Accordingly we designed mechanisms to encourage the firms to invest at a socially optimal level. The achieved results in this paper helps security designers to better formulate their policies in tackling strategic attackers. © 2018, Springer Science+Business Media, LLC, part of Springer Nature.
Expert Systems with Applications (09574174)88pp. 327-337
Sharing cyber security information helps firms to decrease cyber security risks, prevent attacks, and increase their overall resilience. Hence it affects reducing the social security cost. Although previously cyber security information sharing was being performed in an informal and ad hoc manner, nowadays through development of information sharing and analysis centers (ISACs), cyber security information sharing has become more structured, regular, and frequent. This is while, the privacy risk and information disclosure concerns are still major challenges faced by ISACs that act as barriers in activating the potential impacts of ISACs. This paper provides insights on decisions about security investments and information sharing in consideration of privacy risk and security knowledge growth. By the latest concept i.e. security knowledge growth, we mean fusing the collected security information, adding prior knowledge, and performing extra analyses to enrich the shared information. The impact of this concept on increasing the motivation of firms for voluntarily sharing their sensitive information to authorities such as ISACs has been analytically studied for the first time in this paper. We propose a differential game model in which a linear fusion model for characterizing the process of knowledge growth via the ISAC is employed. The Nash equilibrium of the proposed game including the optimized values of security investment, and the thresholds of data sharing with the price of privacy are highlighted. We analytically find the threshold in which the gain achieved by sharing sensitive information outweighs the privacy risks and hence the firms have natural incentive to share their security information. Moreover, since in this case the threshold of data sharing and the security investment levels chosen in Nash equilibrium may be lower than social optimum, accordingly we design mechanisms which would encourage the firms and lead to a socially optimal outcome. The direct impact of the achieved results is on analyzing the way ISACs can convince firms to share their security information with them. © 2017 Elsevier Ltd
