Security and Privacy Issues in Fog Computing

Ali, Ahmad; Ahmed, Mansoor; Imran, Muhammad; Khattak, Hasan Ali

doi:10.1002/9781119551713.ch5

Cited by 21 publications

(14 citation statements)

References 72 publications

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“…Several studies have discussed about trust and authentication issues in fog but, there is no complete solution to the trust and authentication problems in the fog computing 117 . Some recent studies have stated that trust platform mechanism (TPM) and secure element (SE) can be used in fog computing applications to provide trust utility 118 …”

Section: Open Issues In Fog Computingmentioning

confidence: 99%

A systematic review of task scheduling approaches in fog computing

Bansal

Aggarwal

2022

Trans Emerging Tel Tech

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Due to increased use of IoT devices and data sensors a huge amount of data is being produced for processing in real-time. Fog computing has evolved as a solution for fast processing of data. To complete the data processing as per requirement of user, it is processed at fog nodes which are near the user. To complete work in a specified time with limited resources, task scheduling is performed. With the increased amount of data to be processed, the completion of task within given time in fog computing is a major challenge. So, scheduling of tasks and resources is a very important issue. A lot of research has been undertaken in recent years. In this survey, authors have reviewed various task scheduling algorithms suggested by researchers to meet the user requirements.The focus of this article is on diverse scheduling techniques being deployed in fog computing. An effort has been made to classify existing approaches, research issues and determine significant issues existing in this field. There are four major scheduling categories that are used by researchers namely, static, dynamic, heuristic, and hybrid. As per this study, 17% researchers deployed static, 23% dynamic, 47% heuristic, and 13% hybrid approaches, respectively.Analysis shows that for QoS (Quality of Service) parameters 19% researchers are focused on response time, 18% on cost and energy consumption, and 16% on makespan. For QoS parameter other factors have much smaller contribution in comparison to the above factors. In the tools used, researchers observed that 40% of the researches have used iFogSim, according to literature. Besides, this research article also discusses open issues and future work in the field of fog computing. This article also underlines various open issues and future directions in the field of fog computing.

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Section: Open Issues In Fog Computingmentioning

confidence: 99%

A systematic review of task scheduling approaches in fog computing

Bansal

Aggarwal

2022

Trans Emerging Tel Tech

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“…(i) Something you know (ii) something you have and (iii) something you are, are some known identification variables. Similarly, temporal variables to constraint an individual are (i) only for Identified User, (ii) only for Specified Time, and (iii) only at specified Geo-Location ( Ali et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

“…1 . A detailed taxonomy as developed in Ali et al (2020) covers various other authentication schemes.…”

Section: Introductionmentioning

confidence: 99%

VisTAS: blockchain-based visible and trusted remote authentication system

Ali

Ahmed

Khan

et al. 2021

PeerJ Computer Science

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The information security domain focuses on security needs at all levels in a computing environment in either the Internet of Things, Cloud Computing, Cloud of Things, or any other implementation. Data, devices, services, or applications and communication are required to be protected and provided by information security shields at all levels and in all working states. Remote authentication is required to perform different administrative operations in an information system, and Administrators have full access to the system and may pose insider threats. Superusers and administrators are the most trusted persons in an organisation. “Trust but verify” is an approach to have an eye on the superusers and administrators. Distributed ledger technology (Blockchain-based data storage) is an immutable data storage scheme and provides a built-in facility to share statistics among peers. Distributed ledgers are proposed to provide visible security and non-repudiation, which securely records administrators’ authentications requests. The presence of security, privacy, and accountability measures establish trust among its stakeholders. Securing information in an electronic data processing system is challenging, i.e., providing services and access control for the resources to only legitimate users. Authentication plays a vital role in systems’ security; therefore, authentication and identity management are the key subjects to provide information security services. The leading cause of information security breaches is the failure of identity management/authentication systems and insider threats. In this regard, visible security measures have more deterrence than other schemes. In this paper, an authentication scheme, “VisTAS,” has been introduced, which provides visible security and trusted authentication services to the tenants and keeps the records in the blockchain.

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“…In this paper, we propose to build the data set with ample information which can be well perceive and understand. The visualization of such data set, make it easy to distinguish between normal and abnormal activity of a malware, instead of traditional inadequate data sets [21]. The advance and latest malware samples (e.g., fund transfer, Korplug, CTB locker, etc.)…”

Section: Introductionmentioning

confidence: 99%

FIViz: Forensics Investigation through Visualization for Malware in Internet of Things

et al. 2020

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Adoption of the Internet of Things for the realization of smart cities in various domains has been pushed by the advancements in Information Communication and Technology. Transportation, power delivery, environmental monitoring, and medical applications are among the front runners when it comes to leveraging the benefits of IoT for improving services through modern decision support systems. Though with the enormous usage of the Internet of Medical Things, security and privacy become intrinsic issues, thus adversaries can exploit these devices or information on these devices for malicious intents. These devices generate and log large and complex raw data which are used by decision support systems to provide better care to patients. Investigation of these enormous and complicated data from a victim’s device is a daunting and time-consuming task for an investigator. Different feature-based frameworks have been proposed to resolve this problem to detect early and effectively the access logs to better assess the event. But the problem with the existing approaches is that it forces the investigator to manually comb through collected data which can contain a huge amount of irrelevant data. These data are provided normally in textual form to the investigators which are too time-consuming for the investigations even if they can utilize machine learning or natural language processing techniques. In this paper, we proposed a visualization-based approach to tackle the problem of investigating large and complex raw data sets from the Internet of Medical Things. Our contribution in this work is twofold. Firstly, we create a data set through a dynamic behavioral analysis of 400 malware samples. Secondly, the resultant and reduced data set were then visualized most feasibly. This is to investigate an incident easily. The experimental results show that an investigator can investigate large amounts of data in an easy and time-efficient manner through the effective use of visualization techniques.

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Security and Privacy Issues in Fog Computing

Cited by 21 publications

References 72 publications

A systematic review of task scheduling approaches in fog computing

A systematic review of task scheduling approaches in fog computing

VisTAS: blockchain-based visible and trusted remote authentication system

FIViz: Forensics Investigation through Visualization for Malware in Internet of Things

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