The Internet of Things: Multi-faceted Research Perspectives

Shim, Jung P.; Sharda, Ramesh; French, Aaron M.; Syler, Rhonda A.; Patten, Karen

doi:10.17705/1cais.04621

Cited by 19 publications

(15 citation statements)

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“…With our work, we want to draw attention to the growing need to pay more attention to U&P in practice. We contribute to the pressing problem of unpatched IoT devices [3], [82] and identify factors that influence their patch status. The results suggest that devices of particular manufacturers are updated much more frequently than those of others.…”

Section: Discussion and Contributionmentioning

confidence: 99%

A Large-Scale Analysis of IoT Firmware Version Distribution in the Wild

Ebbers

2023

IIEEE Trans. Software Eng.

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This paper examines the up-to-dateness of installed firmware versions of Internet of Things devices accessible via public Internet. It takes a novel approach to identify versions based on the source code of their web interfaces. It analyzes data sets of 1.06m devices collected using the IoT search engine Censys and then maps the results against the latest version each manufacturer offers. A fully scalable and adaptive approach is developed by applying the SEMMA data mining process. This approach relies on three data artifacts: raw data from Censys, a mapping table with firmware versions, and a keyword search list. The results confirm the heterogeneity of connected IoT devices and show that only 2.45 percent of the IoT devices "in the wild" run the latest available firmware. Installed versions are 19.2 months old on average. This real-world evidence suggests that the updating processes and methods used by engineers so far are not sufficient to keep IoT devices up-to-date. This paper identifies and quantifies influencing factors and captures the global and diverse distribution of IoT devices. It finds manufacturer and device type influence the up-todateness of firmware, whereas the country in which the device is deployed is less significant.

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Section: Discussion and Contributionmentioning

confidence: 99%

A Large-Scale Analysis of IoT Firmware Version Distribution in the Wild

Ebbers

2023

IIEEE Trans. Software Eng.

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“…Our work relates to three domains of related work: model-driven security engineering, attack/defense graphs, and architecture modeling for system analysis. 1 https://www.cnet.com/news/solarwinds-hack-officially-blamed-on-russia-what-you-need-toknow/ 2 https://www.industrialdefender.com/florida-water-treatment-plant-cyber-attack/ 3 https://www.cbsnews.com/news/millions-facebook-user-records-exposed-amazon-cloud-server/…”

Section: Related Workmentioning

confidence: 99%

“…If a source collected for a keyword is classified as Blogs by ST and Less than 1 year by TP, then we applied their parameterized heuristic values P(ST = Blogs) and P(TP = Less than 1 year) from Table 1. Next, we calculated the overall credibility C source according to Equation (1). For instance, the source [46] was collected for the keyword resourceHijacking and was classified into Analyst papers and Less than 1 year.…”

Section: Evaluate Information Sources By Credibility Assessmentmentioning

confidence: 99%

“…Cyber security continues to be a key concern and a fundamental aspect of information technology (IT) and operational technology (OT) systems [1]. Recent years have witnessed some of the largest, most sophisticated, and most severe cyber attacks, such as the SolarWinds attack 1 , Florida water supply attack 2 , and Facebook information leak 3 , which affected millions of consumers and thousands of businesses.…”

Section: Introductionmentioning

confidence: 99%

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A Method for Assigning Probability Distributions in Attack Simulation Languages

Xiong

Hacks

Lagerström

2021

CSIMQ

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Cyber attacks on IT and OT systems can have severe consequences for individuals and organizations, from water or energy distribution systems to online banking services. To respond to these threats, attack simulations can be used to assess the cyber security of systems to foster a higher degree of resilience against cyber attacks; the steps taken by an attacker to compromise sensitive system assets can be traced, and a time estimate can be computed from the initial step to the compromise of assets of interest. Previously, the Meta Attack Language (MAL) was introduced as a framework to develop security-oriented domain-specific languages. It allows attack simulations on modeled systems and analyzes weaknesses related to known attacks. To produce more realistic simulation results, probability distributions can be assigned to attack steps and defenses to describe the efforts required for attackers to exploit certain attack steps. However, research on assessing such probability distributions is scarce, and we often rely on security experts to model attackers' efforts. To address this gap, we propose a method to assign probability distributions to the attack steps and defenses of MAL-based languages. We demonstrate the proposed method by assigning probability distributions to a MAL-based language. Finally, the resulting language is evaluated by modeling and simulating a known cyber attack.

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“…In several IoT applications, the process of analysis must be done instantly, and response or preventive action must be taken immediately, which means that the opportunity to prevent potential damage decreases during the data transmission from the device to the cloud for analysis. Cisco has published a report about such a difficult challenge that disrupts IoT systems, and as the report indicates, IoT systems need to a developed new computing model to deal with the variety, rapidity, and quantity of IoT data [7]. Therefore, Instead of using centralized clouds, decentralized resources at the network's edges can be used to process data that is close to the users in fog computing [8,9].…”

Section: Introductionmentioning

confidence: 99%

A Novel Approach for Traffic Congestion Mitigation Based on Fog Computing Using IFogsimulator

Elsayed

Mohamed

Harb

2022

Preprint

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Internet of Things (IoT) needs an accurate latency computation. The connected IoT devices generate an enormous amount of data which is processed on the infrastructure of a cloud. However, the cloud is not an effective solution. To address this issue, fog computing enables processing at the edge while still allowing users to communicate with the cloud. Therefore, many applications depend on fog computing, such as traffic management. In this paper, an intelligent Traffic Congestion Mitigation System (ITCMS) is proposed to solve the problem of traffic congestion in crowded smart cities. The proposed system is implemented based on fog computing. It has been applied to a crowded city and its performance is evaluated according to some metrics such as traffic efficiency, saving energy and reducing the latency, the average rate of traffic flow, and waiting time. The obtained results show that the execution time of our simulation takes 4,548 seconds, the Application loop delay takes 49.67 seconds. Thus, the results show better performance, more accuracy, low latency, and traffic efficiency

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The Internet of Things: Multi-faceted Research Perspectives

Cited by 19 publications

References 0 publications

A Large-Scale Analysis of IoT Firmware Version Distribution in the Wild

A Large-Scale Analysis of IoT Firmware Version Distribution in the Wild

A Method for Assigning Probability Distributions in Attack Simulation Languages

A Novel Approach for Traffic Congestion Mitigation Based on Fog Computing Using IFogsimulator

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