Security and trust issues in Fog computing: A survey

Zhang, Peiyun; Zhou, MengChu; Fortino, Giancarlo

doi:10.1016/j.future.2018.05.008

Cited by 299 publications

(130 citation statements)

References 61 publications

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“…Zhang et al [21] discussed and analyzed the architecture of fog computing and pointed out related potential security and trust issues. They comprehensively reported on how to solve these problems in the existing literature and discussed open challenges in security and trustworthiness of fog computing, research trends and future topics.…”

Section: Relevant Workmentioning

confidence: 99%

An Efficient and Credible Multi-Source Trust Fusion Mechanism Based on Time Decay for Edge Computing

Kong

Hou

et al. 2020

Electronics

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With the development of 5G, user terminal computing moves up and cloud computing sinks, thus forming a computing fusion at the edge. Edge computing with high-efficiency, real-time, and fast features will become part of 5G construction. Utilizing distributed computing and storage resources at the edge of the network to perform distributed data processing tasks can alleviate the load on the cloud computing center, which is also the development trend of edge computing. When a malicious node exists, the error information feedback by the node will affect the result of local perception decision. To solve the problem of malicious behavior of the node, a node trust evaluation mechanism of interactive behavior is introduced. The trust mechanism for edge computing network environments is introduced as a novel security solution. First, the key thought of the trust mechanism proposed in this paper is to establish a trust relationship between edge nodes in open edge computing environment. Then, a multi-source trust fusion algorithm based on time decay aggregates direct interaction trust and different third-party recommendation trust to calculate the global trust of the evaluated nodes. Finally, simulation experiments show that the algorithm has a certain degree of improvement in computational efficiency and interaction success rate over other existing models, which reduces the situation of malicious node deception.

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Section: Relevant Workmentioning

confidence: 99%

An Efficient and Credible Multi-Source Trust Fusion Mechanism Based on Time Decay for Edge Computing

Kong

Hou

et al. 2020

Electronics

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“…Although there is some concrete progress in the NFV technologies, predominant focus has been on management and orchestration aspects. A comprehensive review of major security challenges for IoT, fog computing architecture and corresponding solutions in the IoT layered architecture is presented by Khan and Salah and Zhang et al Morabito et al proposed a lightweight gateway mechanism called “LegoIT” at the Edge/IoT networks and demonstrated security applications. Rapuzzi and Repetto articulated methods to build defense mechanisms with “situational awareness” for fog computing infrastructures.…”

Section: Related Workmentioning

confidence: 99%

“…Although there is some concrete progress in the NFV technologies, predominant focus has been on management and orchestration aspects. A comprehensive review of major security challenges for IoT, fog computing architecture and corresponding solutions in the IoT layered architecture is presented by Khan and Salah 23 and Zhang et al 24 Morabito et al 25 Chakrabarty et al 30 proposed "BlackSDN -a secure SDN IoT network architecture" as a solution to protect IoT, and they used "SDN-controller as root-of-trust" to run the encrypted communication between multiple parties. Basile et al 31 aimed to exemplify the "network-function-on-demand as a service" model of the NFV paradigm.…”

Section: Related Workmentioning

confidence: 99%

SDN/NFV security framework for fog‐to‐things computing infrastructure

2019

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Summary Currently, core networking architectures are facing disruptive developments, due to emergence of paradigms such as Software‐Defined‐Networking (SDN) for control, Network Function Virtualization (NFV) for services, and so on. These are the key enabling technologies for future applications in 5G and locality‐based Internet of things (IoT)/wireless sensor network services. The proliferation of IoT devices at the Edge networks is driving the growth of all‐connected world of Internet traffic. In the Cloud‐to‐Things continuum, processing of information and data at the Edge mandates development of security best practices to arise within a fog computing environment. Service providers are transforming their business using NFV‐based services and SDN‐enabled networks. The SDN paradigm offers an easily programmable model, global view, and control for modern networks, which demand faster response to security incidents and dynamically enforce countermeasures to intrusions and cyberattacks. This article proposes an autonomic multilayer security framework called Distributed Threat Analytics and Response System (DTARS) for a converged architecture of Fog/Edge computing and SDN infrastructures, for emerging applications in IoT and 5G networks. The major detection scheme is deployed within the data plane, consisting of a coarse‐grained behavioral, anti‐spoofing, flow monitoring and fine‐grained traffic multi‐feature entropy‐based algorithms. We developed exemplary defense applications under DTARS framework, on a malware testbed imitating the real‐life DDoS/botnets such as Mirai. The experiments and analysis show that DTARS is capable of detecting attacks in real‐time with accuracy more than 95% under attack intensities up to 50 000 packets/s. The benign traffic forwarding rate remains unaffected with DTARS, while it drops down to 65% with traditional NIDS for advanced DDoS attacks. Further, DTARS achieves this performance without incurring additional latency due to data plane overhead.

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“…To address these challenges, Fog computing architecture is introduced. It brings the cloud services much closer to IoT devices/end‐devices . In Figure , we discuss the Fog computing architecture.…”

Section: Introductionmentioning

confidence: 99%

“…It brings the cloud services much closer to IoT devices/end-devices. [17][18][19][20] In Figure 2, we discuss the Fog computing architecture. The Fog computing architecture is a three-layer schema.…”

Section: Introductionmentioning

confidence: 99%

Fog computing–based node‐to‐node communication and mobility management technique for 5G networks

Babu

Biswash

2019

Trans Emerging Tel Tech

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In this paper, we present a Fog computing–based node‐to‐node communication architecture for 5G networks. It supports the minimum network infrastructure dependent communication. In order to establish the connection, associated next generation evolve node base station (gNB) imports the required information from nearby Fog servers. The retrieved pieces of information are processed in data analytic units (DAUs). The DAUs are in‐build processing units and closely tie‐up with Fog servers/gNBs. The Fog servers and data analytic units are frequently deployed in the coverage area. They always offer and receive the services from gNBs to establish node‐to‐node communication. A robust mobility management scheme is also proposed for dynamic mobile users to achieve node‐to‐node communication. The signaling cost, energy consumption, delay, and latency are the performance parameters for the proposed work. The numerical and system level simulation results are produced to validate the proposed contributions. We compare the obtained results with 4G/LTE cellular networks in order to check the effectiveness of work. We achieved 26% to 63% of performance improvement as compared to legacy networks w.r.t. listed parameters.

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Security and trust issues in Fog computing: A survey

Cited by 299 publications

References 61 publications

An Efficient and Credible Multi-Source Trust Fusion Mechanism Based on Time Decay for Edge Computing

An Efficient and Credible Multi-Source Trust Fusion Mechanism Based on Time Decay for Edge Computing

SDN/NFV security framework for fog‐to‐things computing infrastructure

Fog computing–based node‐to‐node communication and mobility management technique for 5G networks

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