Over the last few years, the number of smart objects connected to the Internet has grown exponentially in comparison to the number of services and applications. The integration between Cloud Computing and Internet of Things, named as Cloud of Things, plays a key role in managing the connected things, their data and services. One of the main challenges in Cloud of Things is the resource discovery of the smart objects and their reuse in different contexts. Most of the existent work uses some kind of multi-criteria decision analysis algorithm to perform the resource discovery, but do not evaluate the impact that the user constraints has in the final solution. In this paper, we analyse the behaviour of the SAW, TOPSIS and VIKOR multi-objective decision analyses algorithms and the impact of user constraints on them. We evaluated the quality of the proposed solutions using the Pareto-optimality concept.
@cite_27 use domain name server as a scalable metadata repository to support the entity discovery using their location. The authors proposes the creation of a new domain, such as , which represent the entities of the real world. Thus, when a smart object becomes available it must register their characteristics and services into the DNS repository.
@cite_27
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Name services are critical for mapping logical resource names to physical resources in large-scale distributed systems. The Domain Name System (DNS) used on the Internet, however, is slow, vulnerable to denial of service attacks, and does not support fast updates. These problems stem fundamentally from the structure of the legacy DNS.This paper describes the design and implementation of the Cooperative Domain Name System (CoDoNS), a novel name service, which provides high lookup performance through proactive caching, resilience to denial of service attacks through automatic load-balancing, and fast propagation of updates. CoDoNS derives its scalability, decentralization, self-organization, and failure resilience from peer-to-peer overlays, while it achieves high performance using the Beehive replication framework. Cryptographic delegation, instead of host-based physical delegation, limits potential malfeasance by namespace operators and creates a competitive market for namespace management. Backwards compatibility with existing protocols and wire formats enables CoDoNS to serve as a backup for legacy DNS, as well as a complete replacement. Performance measurements from a real-life deployment of the system in PlanetLab shows that CoDoNS provides fast lookups, automatically reconfigures around faults without manual involvement and thwarts distributed denial of service attacks by promptly redistributing load across nodes. The domain name service (DNS) plays an important role in the operation of the Internet, providing a two-way mapping between domain names and their numerical identifiers. Given its fundamental role, it is not surprising that a wide variety of malicious activities involve the domain name service in one way or another. For example, bots resolve DNS names to locate their command and control servers, and spam mails contain URLs that link to domains that resolve to scam servers. Thus, it seems beneficial to monitor the use of the DNS system for signs that indicate that a certain name is used as part of a malicious operation. In this paper, we introduce EXPOSURE, a system that employs large-scale, passive DNS analysis techniques to detect domains that are involved in malicious activity. We use 15 features that we extract from the DNS traffic that allow us to characterize different properties of DNS names and the ways that they are queried. Our experiments with a large, real-world data set consisting of 100 billion DNS requests, and a real-life deployment for two weeks in an ISP show that our approach is scalable and that we are able to automatically identify unknown malicious domains that are misused in a variety of malicious activity (such as for botnet command and control, spamming, and phishing). The domain name system (DNS) is a core component of the Internet. It performs the vital task of mapping human readable names into machine readable data (such as IP addresses, which hosts handle e-mail, and so on). The content of the DNS reveals a lot about the technical operations of a domain. Thus, studying the state of large parts of the DNS over time reveals valuable information about the evolution of the Internet. We collect a unique long-term data set with daily DNS measure-ments for all the domains under the main top-level domains (TLDs) on the Internet (including .com , .net , and .org , comprising 50 of the global DNS name space). This paper discusses the challenges of performing such a large-scale active measurement. These challenges include scaling the daily measurement to collect data for the largest TLD ( .com , with 123M names) and ensuring that a measurement of this scale does not impose an unacceptable burden on the global DNS infrastructure. The paper discusses the design choices we have made to meet these challenges and documents the design of the measurement system we implemented based on these choices. Two case studies related to cloud e-mail services illustrate the value of measuring the DNS at this scale. The data this system collects is valuable to the network research community. Therefore, we end this paper by discussing how we make the data accessible to other researchers. The Domain Name System (DNS) is an essential protocol used by both legitimate Internet applications and cyber attacks. For example, botnets rely on DNS to support agile command and control infrastructures. An effective way to disrupt these attacks is to place malicious domains on a "blocklist" (or "blacklist") or to add a filtering rule in a firewall or network intrusion detection system. To evade such security countermeasures, attackers have used DNS agility, e.g., by using new domains daily to evade static blacklists and firewalls. In this paper we propose Notos, a dynamic reputation system for DNS. The premise of this system is that malicious, agile use of DNS has unique characteristics and can be distinguished from legitimate, professionally provisioned DNS services. Notos uses passive DNS query data and analyzes the network and zone features of domains. It builds models of known legitimate domains and malicious domains, and uses these models to compute a reputation score for a new domain indicative of whether the domain is malicious or legitimate. We have evaluated Notos in a large ISP's network with DNS traffic from 1.4 million users. Our results show that Notos can identify malicious domains with high accuracy (true positive rate of 96.8 ) and low false positive rate (0.38 ), and can identify these domains weeks or even months before they appear in public blacklists.