Recent Security Trends in Internet of Things: A Comprehensive Survey

Ensuring security for modern IoT systems requires the use of complex methods to analyze their software. One of the most in-demand methods that has repeatedly been proven to be effective is static analysis. However, the progressive complication of the connections in IoT systems, the increase in their scale, and the heterogeneity of elements requires the automation and intellectualization of manual experts’ work. A hypothesis to this end is posed that assumes the applicability of machine-learning solutions for IoT system static analysis. A scheme of this research, which is aimed at confirming the hypothesis and reflecting the ontology of the study, is given. The main contributions to the work are as follows: systematization of static analysis stages for IoT systems and decisions of machine-learning problems in the form of formalized models; review of the entire subject area publications with analysis of the results; confirmation of the machine-learning instrumentaries applicability for each static analysis stage; and the proposal of an intelligent framework concept for the static analysis of IoT systems. The novelty of the results obtained is a consideration of the entire process of static analysis (from the beginning of IoT system research to the final delivery of the results), consideration of each stage from the entirely given set of machine-learning solutions perspective, as well as formalization of the stages and solutions in the form of “Form and Content” data transformations.

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“…In [ 67 ], the security trends for IoT devices are assessed. A total of 20 surveys are reviewed, and ML is considered in only 25% of them.…”

Section: Analysis Of Existing Review Workmentioning

confidence: 99%

Static Analysis of Information Systems for IoT Cyber Security: A Survey of Machine Learning Approaches

Kotenko

Izrailov

Buinevich

2022

Sensors

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“…It is capable to ensure the integrity of the software and configuration of all the nodes and, thus, to avoid the exchange of incorrect synchronization information over secure protocols. For a more complete view on prior works about SW vulnerabilities in other domains, interested readers can also refer to [29][30][31] and the references therein.…”

Section: Master Clockmentioning

confidence: 99%

Trusted GNSS-Based Time Synchronization for Industry 4.0 Applications

Margaria

Vesco

2021

Applied Sciences

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The protection of satellite-derived timing information is becoming a fundamental requirement in Industry 4.0 applications, as well as in a growing number of critical infrastructures. All the industrial systems where several nodes or devices communicate and/or coordinate their functionalities by means of a communication network need accurate, reliable and trusted time synchronization. For instance, the correct operation of automation and control systems, measurement and automatic test systems, power generation, transmission, and distribution typically require a sub-microsecond time accuracy. This paper analyses the main attack vectors and stresses the need for software integrity control at network nodes of Industry 4.0 applications to complement existing security solutions that focus on Global Navigation Satellite System (GNSS) radio-frequency spectrum and Precision Time Protocol (PTP), also known as IEEE-1588. A real implementation of a Software Integrity Architecture in accordance with Trusted Computing principles concludes the work, together with the presentation of promising results obtained with a flexible and reconfigurable testbed for hands-on activities.

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“…Previous challenges can be dealt with by fog computing [ 9 ]. According to the prediction by Cisco, approximately 1 trillion IoT devices will be linked to the internet by 2025 [ 10 ]. Resource management in the fog computing environment has become a big concern due to changes in the devices of users in terms of bandwidth, computation, latency, and storage [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

A Novel Low-Latency and Energy-Efficient Task Scheduling Framework for Internet of Medical Things in an Edge Fog Cloud System

Alatoun

Matrouk

Mohammed

et al. 2022

Sensors

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In healthcare, there are rapid emergency response systems that necessitate real-time actions where speed and efficiency are critical; this may suffer as a result of cloud latency because of the delay caused by the cloud. Therefore, fog computing is utilized in real-time healthcare applications. There are still limitations in response time, latency, and energy consumption. Thus, a proper fog computing architecture and good task scheduling algorithms should be developed to minimize these limitations. In this study, an Energy-Efficient Internet of Medical Things to Fog Interoperability of Task Scheduling (EEIoMT) framework is proposed. This framework schedules tasks in an efficient way by ensuring that critical tasks are executed in the shortest possible time within their deadline while balancing energy consumption when processing other tasks. In our architecture, Electrocardiogram (ECG) sensors are used to monitor heart health at home in a smart city. ECG sensors send the sensed data continuously to the ESP32 microcontroller through Bluetooth (BLE) for analysis. ESP32 is also linked to the fog scheduler via Wi-Fi to send the results data of the analysis (tasks). The appropriate fog node is carefully selected to execute the task by giving each node a special weight, which is formulated on the basis of the expected amount of energy consumed and latency in executing this task and choosing the node with the lowest weight. Simulations were performed in iFogSim2. The simulation outcomes show that the suggested framework has a superior performance in reducing the usage of energy, latency, and network utilization when weighed against CHTM, LBS, and FNPA models.

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Recent Security Trends in Internet of Things: A Comprehensive Survey

Cited by 55 publications

References 105 publications

Static Analysis of Information Systems for IoT Cyber Security: A Survey of Machine Learning Approaches

Static Analysis of Information Systems for IoT Cyber Security: A Survey of Machine Learning Approaches

Trusted GNSS-Based Time Synchronization for Industry 4.0 Applications

A Novel Low-Latency and Energy-Efficient Task Scheduling Framework for Internet of Medical Things in an Edge Fog Cloud System

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