Internet of Things (IoT) has not yet reached a distinctive definition. A generic understanding of IoT is that it offers numerous services in many domains, utilizing conventional internet infrastructure by enabling different communication patterns such as human-to-object, object-to-objects, and object-to-object. Integrating IoT objects into the standard Internet, however, has unlocked several security challenges, as most internet technologies and connectivity protocols have been specifically designed for unconstrained objects. Moreover, IoT objects have their own limitations in terms of computation power, memory and bandwidth. IoT vision, therefore, has suffered from unprecedented attacks targeting not only individuals but also enterprises, some examples of these attacks are loss of privacy, organized crime, mental suffering, and the probability of jeopardizing human lives. Hence, providing a comprehensive classification of IoT attacks and their available countermeasures is an indispensable requirement. In this paper, we propose a novel four-layered IoT reference model based on building blocks strategy, in which we develop a comprehensive IoT attack model composed of four key phases. First, we have proposed IoT asset-based attack surface, which consists of four main components: 1) physical objects, 2) protocols covering whole IoT stack, 3) data, and 4) software. Second, we describe a set of IoT security goals. Third, we identify IoT attack taxonomy for each asset. Finally, we show the relationship between each attack and its violated security goals, and identify a set of countermeasures to protect each asset as well. To the best of our knowledge, this is the first paper that attempts to provide a comprehensive IoT attacks model based on a building-blocked reference model.
This paper describes the design and development of a novel cloud-based system for increased service contextualization in future wireless networks. The principal objective is the support of mobile users (consumers) in a Ubiquitous Consumer Wireless World (UCWW) seeking to choose and select the 'best' service instance in a UCWW environment matched to their dynamic contextualized and personalized service delivery requirements and expectations, thereby increasing user freedom in where, when and how they access desired services, and increasing user-driven networking. The design challenges to create such a cloud-based system with an ever-enhanced capacity to be attuned to a user-client's dynamic contexts, and do this for all its users, are addressed, and software infrastructural design solutions suggested. The cloud idea proposed here is one which should yield efficiencies and saving for consumers, operate as an additional 'behind-the-scenes' decision support subsystem to make smart decisions based on mining of the most up-to-date data stored in the cloud repositories related to service contexts and personalized profiles. Rather than the use of known efficient heuristic methods employed with large and complex data structures, together with associated algorithms solving the combinatorial optimization problems, an alternative method, proposed here for making predictions, is to discover patterns in the behaviour of the individual clientconsumer, to bring into play, in the decision process, patterns and trends of other client-consumers seeking the same or similar services, and also the constant update of the user's wireless environment context through information garnered from other sources, such as wireless access service provider updates, teleservice provider updates, and data sensed by the sensors in the environment. Indentifying and addressing the need as directly as this is a novel approach towards providing context-aware personalized services. It is particularly novel, and desirable, in the UCWW context. Hence, this consumer supportive smart repository solution may appropriately be called a UCWW cloud. The paper sets out an infrastructural design of this cloud, ordered within a conceptual UCWW software architecture, together with its various elements, e.g., decision support subsystem and mobile network environment elements of personalized information retrieval (PIR).
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