The Internet of Things (IoT) has become a global sensory network that links physical and virtual objects by communicating and exploiting data and initiating physical actions. The evolution of this paradigm is already threatened by security issues, which constitute major risk factors that demand efficient solutions adapted to the IoT context. In this paper, we put forward a logical approach and systemic analysis that enables us to present the key aspects of new access control (AC) model for the IoT environments, called a pervasive-based access control model (PerBAC). Our approach is based on the study of important, reputable AC models that we use as a background for our proposed model. PerBAC is defined here based on a representation of the decision-making algorithm, a description of the abstract entities using the attributes as a fundamental concept and the collaboration aspects necessary to handle the case of multiple organizations. These attributes are the perfect recipient of the information collected by IoT environments from the physical world and allow optimal access control decisions to be taken according to dynamic rules and entities based on the algorithm. Our interpretation of the attributes, the dynamic entities and their exploitation by our proposed algorithm produce a new AC model adapted to the IoT paradigm.
The transition from the current Internet to the Internet of things (IoT) is inevitable and is already necessary. The main aspect about the IoT paradigm is the integration of several technologies and standards. However, what places have the security and privacy in this paradigm?We present the necessary background by introducing the IoT paradigm. Then, we summarize the standards and enabling technologies that refer to security in the IoT. Furthermore, we point some major issues that should be faced by the research community related to the security in the IoT. Finally, based on our evaluation, we highlight some possible directions for future research. Our goal is not only to analyze, compare and consolidate past research work but also to appreciate their findings and discuss their applicability towards securing the IoT and to provide a solid base to start a scientific research around security and privacy on the IoT.
The Internet of things is no longer a concept; it is a reality already changing our lives. It aims to interconnect almost all daily used devices to help them exchange contextualized data in order to offer services adequately. Based on the existing Internet, IoT suffers indisputably from security issues that could threaten its evolution and its users' interests. Starting from this fact, we try to define the main security threats for the IoT perimeter and propose some pertinent solutions. To do so, we first establish a state of the art concerning the IoT definition, protocols, environment, architecture and security. Then, we expose a case study of a standard IoT platform to illustrate the impact of security on all IoT layers. Furthermore, the paper presents the results of a security audit on our implemented platform. Finally, based on our evaluation, we highlight many solutions as well as possible directions for future research.
-Cloud computing is a new way of integrating a set of old technologies to implement a new paradigm that creates an avenue for users to have access to shared and configurable resources through internet on-demand. This system has many common characteristics with distributed systems, hence, the cloud computing also uses the features of networking. Thus the security is the biggest issue of this system, because the services of cloud computing is based on the sharing. Thus, a cloud computing environment requires some intrusion detection systems (IDSs) for protecting each machine against attacks. The aim of this work is to present a classification of attacks threatening the availability, confidentiality and integrity of cloud resources and services. Furthermore, we provide literature review of attacks related to the identified categories. Additionally, this paper also introduces related intrusion detection models to identify and prevent these types of attacks.
Personalized e-learning systems based on recommender systems refines enormous amount of data and provides suggestions on learning resources which is appealing to the learner. Although, the recommender systems depends on content based approach or collaborative filtering technique to make recommendations, these methods suffers from cold start and data sparsity problems. To overcome the limitations of the aforementioned problems, a weight based approach is proposed for better performance. The main criterion for building a personalized recommender system is to exploit useful content and provide better recommendations with minimal processing time. The proposed system is a web based client side application which uses user profiles to form neighborhoods and calculates predictions using weights. For newcomers a profile is constructed based on learning styles. The resources which might be of interest to the user are predicted from calculated predictions.
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