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The proliferation of Internet of Things (IoT) markets in the last decade introduces new challenges for network traffic analysis, and processing packet flows to identify IoT devices. This type of device suffers from scarcity, making them vulnerable to spoofing operations. In such circumstances, the device can be recognized by identifying its fingerp...
Citations
... The notion of the intelligent transportation system (ITS) appeared to enhance the output of traffic systems, improve road traffic safety and protect the environment [48]- [50]. The advancement of sensing and broadcasting technologies, as well as the development of efficient incorporation of networked information systems, decision-making, and infrastructure of physics, all contributed significantly to the appearance of ITS [47], [51]- [53]. However, with increasing traffic density, especially in developing countries, an intelligent transportation system needs an auxiliary system to control and increase the safety and sustainability of the transport system, and Big Data can meet these requirements. ...
Big Data technology is emerging as a mass technology that can be applied to many industries in life. Decisions in a wide range of fields may benefit greatly from the information provided by Big Data and Analytics research. One of the areas that have benefited the most from this technology is transportation, which is known as an important field in the development of each nation and possesses a huge treasure of data that traditional technologies cannot handle. Indeed, many countries have applied Big Data-based intelligent transportation systems because it is a traffic system that interacts with vehicles and people on the road, thereby reducing traffic congestion and traffic accidents year by year in many countries. The article presents the applications of Big Data technology in smart traffic systems, thereby providing the perspective of a smart city with a smart traffic system as a critical factor. This paper's analysis indicated that smart cities could be born and further developed through the linkage of Big Data technology and smart traffic systems with smart traffic systems as the core. In addition, the results also showed that the obstacle that needs to be studied at this time is the policy and legal framework for Big Data technology. Therefore, a system managed by the state or shared between the state and the private sector should be studied in the future, aiming to harmonize interests and develop the system extensively.
Modernization and technological advancement have made smart and convenient living environments, including smart houses and smart cities, possible, by combining the Internet of Things (IoT), data, and internet-based services over various communication protocols. IoT is the next generation of the Internet. However, commonly resource-constraint IoT devices that are designed to perform a specific purpose, impose new security challenges, including node forgery, unauthorized access of data, and denial of services. They are more susceptible to being compromised by adversaries as opposed to general-purpose computing devices, and are exposed to different kinds of attacks, including spoofing and botnet attacks. Device identification is one of the promising approaches for improving network security. Devices can be identified either using explicit identifiers (internet protocol/media access control addresses) or implicit identifiers (network traffic and radio signal features), with implicit identifiers being more reliable, robust, and secure for device fingerprinting (DFP). In this paper, DFP methods have been studied in detail, with features generated from their communication traffic characteristics, including network traffic traces, IEEE 802.11 MAC frames, and radio signals, discussed. Additionally, key limitations and research challenges have been studied in the context of the IoT paradigm. Research challenges within the context of DFP and the future of IoT technologies are also discussed to shape future directions of work in the area. The key contribution of this study is the identification of different DFP research scopes in the domain of the IoT paradigm, which can be designed and implemented toward the development of IoT network security.
