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A roadmap for security challenges in the Internet of Things

Digital Communications and Networks 2018年第2期4卷 118-137页

作者： Arbia Riahi Sfar Enrico Natalizio Yacine Challal zied chtourou VRIT Lab - Military Academy Nabeul Tunisia Sorbonne Universites Universite de technologie de Compiegne CNRS Heudiasyc UMR 7253 CS 60319 60203 Compiegne Cedex France Laboratoire de Methodes de Conception de Systemes (LMCS) Ecole Nationale Superieure d'Informatique (ESI) Centre de Recherche sur I'Information Scientifique et Technique (CERIST) Algiers Algeria

Unquestionably, communicating entities （object, or things） in the Internet of Things （IoT） context are playing an active role in human activities, systems and processes. The high connectivity of intelligent objects and their severe constraints lead to many security challenges, which are not included in the classical formulation of security problems and solutions. The Security Shield for IoT has been identified by DARPA （Defense Advanced Research Projects Agency） as one of the four projects with a potential impact broader than the Internet itself. To help interested researchers contribute to this research area, an overview of the loT security roadmap overview is presented in this paper based on a novel cognitive and systemic approach. The role of each component of the approach is explained, we also study its interactions with the other main components, and their impact on the overall. A case study is presented to highlight the components and interactions of the systemic and cognitive approach. Then, security questions about privacy, trust, identification, and access control are discussed. According to the novel taxonomy of the loT framework, different research challenges are highlighted, important solutions and research activities are revealed, and interesting research directions are proposed. In addition, current stan dardization activities are surveyed and discussed to the ensure the security of loT components and applications.

关键词： Internet of Things Systemic and cognitive approach Security Privacy Trust Identification Access control

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A robust system for melanoma diagnosis using heterogeneous image databases

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Journal of Biomedical Science and Engineering 2010年第6期3卷 576-583页

作者： Khaled Taouil zied chtourou Nadra Ben Romdhane 不详

Early diagnosis of melanoma is essential for the fight against this skin cancer. Many melanoma detection systems have been developed in recent years. The growth of interest in telemedicine pushes for the development of offsite CADs. These tools might be used by general physicians and dermatologists as a second advice on submission of skin lesion slides via internet. They also can be used for indexation in medical content image base retrieval. A key issue inherent to these CADs is non-heterogeneity of databases obtained with different apparatuses and acquisition techniques and conditions. We hereafter address the problem of training database heterogeneity by developing a robust methodology for analysis and decision that deals with this problem by accurate choice of features according to the relevance of their discriminative attributes for neural network classification. The digitized lesion image is first of all segmented using a hybrid approach based on morphological treatments and active contours. Then, clinical descriptions of malignancy signs are quantified in a set of features that summarize the geometric and photometric features of the lesion. Sequential forward selection (SFS) method is applied to this set to select the most relevant features. A general regression network (GRNN) is then used for the classification of lesions. We tested this approach with color skin lesion images from digitized slides data base selected by expert dermatologists from the hospital “CHU de Rouen-France” and from the hospital “CHU Hédi Chaker de Sfax-Tunisia”. The performance of the system is assessed using the index area (Az) of the ROC curve (Receiver Operating Characteristic curve). The classification permitted to have an Az score of 89,10%.

关键词： Melanoma Computer Aided Diagnosis System Segmentation Feature Selection Classification Generalized Regression Neural Network

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