Evaluating Critical Security Issues of the IoT World: Present and Future Challenges

Frustaci, Mario; Pace, Pasquale; Aloi, Gianluca; Fortino, Giancarlo

doi:10.1109/jiot.2017.2767291

Cited by 547 publications

(221 citation statements)

References 43 publications

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“…Security remains one of the most important challenges as pointed out by a large number of research papers [13,20,61,[112][113][114][115][116]. Related to security, we focus here on application layer, where it is also necessary to understand the communication challenges related to performance factors such as latency, overhead and packet loss.…”

Section: Securitymentioning

confidence: 99%

“…The area of privacy remains generally under-addressed, including aspects of anonymous communication and censorship applications. While some papers [59, 112,132] tackle general IoT or specific protocol privacy issues the area of privacy still requires major efforts to advance, both in application layer which was the subject of this layer, and in IoT-F2C solutions.…”

Section: Open Issues and Challenges Summarizedmentioning

confidence: 99%

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A Survey of Communication Protocols for Internet of Things and Related Challenges of Fog and Cloud Computing Integration

et al. 2019

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The fast increment in the number of IoT (Internet of Things) devices is accelerating the research on new solutions to make cloud services scalable. In this context, the novel concept of fog computing as well as the combined fog-to-cloud computing paradigm is becoming essential to decentralize the cloud, while bringing the services closer to the end-system. This paper surveys on the application layer communication protocols to fulfill the IoT communication requirements, and their potential for implementation in fog-and cloud-based IoT systems. To this end, the paper first briefly presents potential protocol candidates, including request-reply and publish-subscribe protocols. After that, the paper surveys these protocols based on their main characteristics, as well as the main performance issues, including latency, energy consumption and network throughput. These findings are thereafter used to place the protocols in each segment of the system (IoT, fog, cloud), and thus opens up the discussion on their choice, interoperability and wider system integration. The survey is expected to be useful to system architects and protocol designers when choosing the communication protocols in an integrated IoT-to-fog-to-cloud system architecture. Continuous innovations in hardware, software and connection solutions in the last decade have lead to the expansion of the Internet of Things (IoT) with the number of connected devices growing by the day [1] [2]. The huge amount of data generated by these devices require to find a proper system architecture able to both process and store all the data. While cloud-based architectures are being currently used for that purpose, the new fog computing paradigm is envisioned to scale and optimize the IoT infrastructures [3]. Examples of the cloud-based IoT solutions have been proposed in [4], [5], [6] and a detailed analysis of properties for IoT cloud providers has been conducted in [7]. These studies have shown that cloud computing has the potential to satisfy many IoT requirements, such as monitoring of services, powerful processing of sensor data streams and visualization tasks. On the other hand, fog-based solutions are suited to address real-time processing, fast data response, and latency issues, thus extending the cloud capabilities closer to the edge of the network [8]. Among many factors that will determine the performance in a combined IoT, fog and cloud computing paradigm, the application layer communication, which in turn depends on the selected communication protocols, is one of the main ones.Despite the popularity and wide spread usage of HTTP, the currently used protocols in various domains of IoT, fog and cloud domains are de-facto fragmented with many different solutions. This is due to the different requirements and areas that IoT needs to cover, combining the functionalities of sensors, actuators and computing power with security, connectivity and a myriad of other features. As a result, there is no common agreement on the reference architecture or adopted standards of co...

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Section: Securitymentioning

confidence: 99%

Section: Open Issues and Challenges Summarizedmentioning

confidence: 99%

A Survey of Communication Protocols for Internet of Things and Related Challenges of Fog and Cloud Computing Integration

et al. 2019

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“…Beside the aforementioned areas, several works proposed security solutions specially designed for the IoT or D2D context with regard to confidentiality, data reliability, privacy, access control, and authentication, which are mostly surveyed in previous studies. [22][23][24][25][26][27][28][29][30][31] More related to our paper, some other survey papers review existing works focusing especially on trust models in the IoT 6,9,10,[32][33][34] or D2D 11,35,36 context. In the following, we review these works, regardless of whether they use or not social-related information.…”

Section: Related Workmentioning

confidence: 99%

T‐D2D: A trust model for service offloading in device‐to‐device communication

Movahedi

Hosseini

2019

Trans Emerging Tel Tech

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With the advances in pervasive computing, Internet of things (IoT) has gained considerable attention from both research and industrial communities. While IoT devices are able to provide computational services to other devices via device‐to‐device (D2D) communications, they are not guaranteed to be honest and collaborative. In such a context, the trust model can help to detect malicious service providers. However, malicious nodes may perform trust‐distortion attacks to mislead the trust model. They may perform on‐off attacks to remain undetected or bad‐mouth about others to make it difficult to infer if a contradictory recommendation comes from the on‐off nature of the evaluated node or dishonesty of the recommender. To address these issues, we propose T‐D2D, a lightweight trust model capable to face simultaneous trust‐distortion attacks. T‐D2D evaluates a node's nature using both short‐term and long‐term evaluation intervals to detect different types of on‐off attacks. Moreover, it keeps track of marginal misbehaving over several successive intervals to recognize the nature of suspicious on‐off nodes with light misbehaving attitude. To face bad‐mouthing attackers, T‐D2D limits its dependence on recommendations to when the direct trust is not decisive. Moreover, it evaluates the honesty of a recommender based on the correctness of its recommendations over time. Simulation results prove that T‐D2D exhibits significantly better performance than other counterparts in terms of trust level, correctness of calculated trust, percentage of selected malicious providers, total wasted execution time, and energy consumption in presence of simultaneous trust‐distortion attacks.

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“…Internet of Things (IoT) network is a platform that allows devices to communicate with one another via a wireless connection [1]. As a result, industries have developed many promising applications based on IoT [2].…”

Section: Introductionmentioning

confidence: 99%

A scalable IoT-fog framework for urban sound sensing

Baucas

Spachos

2020

Computer Communications

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Internet of Things (IoT) is a system of interrelated devices that can be used to allow large-scale collection and analysis of data. However, as it grew, IoT networks were not capable of managing the data from these services. As a result, cloud computing was introduced to address the need for datacentres for IoT networks. As the technology evolved, the demand for a proper means of supporting and managing crowdsensing and real-time data increased, and cloud servers could no longer keep up with the large volumes of incoming data. This demand brought rise to fog computing. It became an extension to the cloud and allowed resources to be allocated around the network effectively. Its integration to IoT reduced the strain towards the cloud servers. However, issues in high power consumption at the end device and data management constraints surfaced. This paper proposes two approaches to alleviate these issues to keep fog computing remain as a reliable option for IoT-related applications. We created an IoT-based sensing framework that used an urban sound classification model. Through active low and high power states and resource reallocation, we created a network configuration. We tested this configuration against IoT frameworks that use the default fog and cloud setups. The results improved the framework's end device power consumption and server latency. Overall, with the proposed framework, fog computing was proven to be capable of supporting a scalable IoT framework for urban sound sensing.

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Evaluating Critical Security Issues of the IoT World: Present and Future Challenges

Cited by 547 publications

References 43 publications

A Survey of Communication Protocols for Internet of Things and Related Challenges of Fog and Cloud Computing Integration

A Survey of Communication Protocols for Internet of Things and Related Challenges of Fog and Cloud Computing Integration

T‐D2D: A trust model for service offloading in device‐to‐device communication

A scalable IoT-fog framework for urban sound sensing

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