IoT Device security through dynamic hardware isolation with cloud-Based update

Internet of things (IoT) is leading a new digital age. IoT is regarded as the significant frontier that can improve almost all aspect of our lives. Currently, the IoT technology faces several challenges to academic researchers and industry practitioners, mainly that related with security of data. The objective of this study is to develop a prioritization-based taxonomy of the challenging factors that could hinders the security of IoT. By conducting the literature review and questionnaire survey studies 21 challenging factors were identified that are reported in existing literature and in real-world practices. Moreover, the identified challenging factors are mapped in the core domain of IoT (i.e. smart city, smart home, smart wearable's and smart health care); and apply the fuzzy-AHP approach to rank the identified challenging factors with respect to their criticality for security of IoT technology. The application of fuzzy-AHP is novel in this research area as it is successfully applied in other domains of information technology to address the multi-criterion decision making problems. This study is contributing by providing a prioritization-based taxonomy of the IoT security challenging factors that could help the practitioners and research community to revise and develop the new strategies for the secure IoT.

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“…blood gas analyzer and insulin pump etc. [90]. However, few researches have been conducted in order to prevent hijacking of sensors [90].…”

Section: Device Hijackingmentioning

confidence: 99%

“…[90]. However, few researches have been conducted in order to prevent hijacking of sensors [90]. Though, there need to be developed a model in order to secure medical devices being hijacked.…”

Section: Device Hijackingmentioning

confidence: 99%

A Multicriteria Decision Making Taxonomy of IOT Security Challenging Factors

Akbar¹,

Alsanad

Mahmood

et al. 2021

IEEE Access

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“…Kaur et al [ 35 ] also mentioned that “Hardware Security” is not well studied in the literature when compared to software-related cyber-security problems and solutions. Recently, there are emerging studies on hardware assisted security on IoT related devices such as: building a unified identity verification framework based on PUFs [ 36 ], FPGA hardware security for data centers [ 37 ], and re-configurable hardware-based isolation and protection mechanism (IPM) for IoT devices using cloud [ 38 ]. All these support our effort in introducing hardware security measures for FCGs.…”

Section: Hardware Assisted Security Of Fog Computing Devicesmentioning

confidence: 99%

Hardware Security of Fog End-Devices for the Internet of Things

Bütün

Sari

Österberg

2020

Sensors

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The proliferation of the Internet of Things (IoT) caused new application needs to emerge as rapid response ability is missing in the current IoT end-devices. Therefore, Fog Computing has been proposed to be an edge component for the IoT networks as a remedy to this problem. In recent times, cyber-attacks are on the rise, especially towards infrastructure-less networks, such as IoT. Many botnet attack variants (Mirai, Torii, etc.) have shown that the tiny microdevices at the lower spectrum of the network are becoming a valued participant of a botnet, for further executing more sophisticated attacks against infrastructural networks. As such, the fog devices also need to be secured against cyber-attacks, not only software-wise, but also from hardware alterations and manipulations. Hence, this article first highlights the importance and benefits of fog computing for IoT networks, then investigates the means of providing hardware security to these devices with an enriched literature review, including but not limited to Hardware Security Module, Physically Unclonable Function, System on a Chip, and Tamper Resistant Memory.

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“…These account for 54% of the 21.7 billion active connections worldwide [4]. More than 30 billion IoT devices are expected to be connected to the Internet by 2025 [5,6]. As demand for IoT devices has increased, different hardware and OSs, with diverse communication protocols, standards, and software applications have been created [7].…”

Section: Introductionmentioning

confidence: 99%

A Malware Distribution Simulator for the Verification of Network Threat Prevention Tools

Hwang

Kim

2021

Sensors

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With the expansion of the Internet of Things (IoT), security incidents about exploiting vulnerabilities in IoT devices have become prominent. However, due to the characteristics of IoT devices such as low power and low performance, it is difficult to apply existing security solutions to IoT devices. As a result, IoT devices have easily become targets for cyber attackers, and malware attacks on IoT devices are increasing every year. The most representative is the Mirai malware that caused distributed denial of service (DDoS) attacks by creating a massive IoT botnet. Moreover, Mirai malware has been released on the Internet, resulting in increasing variants and new malicious codes. One of the ways to mitigate distributed denial of service attacks is to render the creation of massive IoT botnets difficult by preventing the spread of malicious code. For IoT infrastructure security, security solutions are being studied to analyze network packets going in and out of IoT infrastructure to detect threats, and to prevent the spread of threats within IoT infrastructure by dynamically controlling network access to maliciously used IoT devices, network equipment, and IoT services. However, there is a great risk to apply unverified security solutions to real-world environments. In this paper, we propose a malware simulation tool that scans vulnerable IoT devices assigned a private IP address, and spreads malicious code within IoT infrastructure by injecting malicious code download command into vulnerable devices. The malware simulation tool proposed in this paper can be used to verify the functionality of network threat detection and prevention solutions.

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IoT Device security through dynamic hardware isolation with cloud-Based update

Cited by 21 publications

References 21 publications

A Multicriteria Decision Making Taxonomy of IOT Security Challenging Factors

A Multicriteria Decision Making Taxonomy of IOT Security Challenging Factors

Hardware Security of Fog End-Devices for the Internet of Things

A Malware Distribution Simulator for the Verification of Network Threat Prevention Tools

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