A Review on IoT Operating Systems

Antony, Amal; Sarika, S.

doi:10.5120/ijca2020920245

Cited by 4 publications

(4 citation statements)

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“…Linux operating systems dominate the landscape of IoT platforms, as indicated by Antony et al [6]. However, this widespread adoption has caused a corresponding increase in malware targeting Linux-based systems.…”

Section: Introductionmentioning

confidence: 99%

Linux IoT Malware Variant Classification Using Binary Lifting and Opcode Entropy

Ramamoorthy,

Gupta,

Shashidhar

et al. 2024

Electronics

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Binary function analysis is fundamental in understanding the behavior and genealogy of malware. The detection, classification, and analysis of Linux IoT malware and its variants present significant challenges due to the wide range of architectures supported by the Linux IoT platform. This study concentrates on static analysis using binary lifting techniques to extract and analyze Intermediate Representation (IR) opcode sequences. We introduce a set of statistical entropy-based features derived from these IR opcode sequences, establishing a practical and straightforward methodology for machine learning classification models. By exclusively analyzing function metadata and opcode entropy, our architecture-agnostic approach not only efficiently detects malware but also classifies its variants with a high degree of accuracy, achieving an F1 score of 97%. The proposed approach offers a robust alternative for enhancing malware detection and variant identification frameworks for IoT devices.

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Section: Introductionmentioning

confidence: 99%

Linux IoT Malware Variant Classification Using Binary Lifting and Opcode Entropy

Ramamoorthy,

Gupta,

Shashidhar

et al. 2024

Electronics

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“…In the context of embedded systems, including IoT devices, it is crucial to have specific implementations of PQC algorithms that are optimized for ARM Cortex M4 and Cortex-A processors. These processors are widely used in embedded systems due to their low power consumption and cost effectiveness [ 52 , 53 , 54 ].…”

Section: Introductionmentioning

confidence: 99%

Role-Driven Clustering of Stakeholders: A Study of IoT Security Improvement

Almalki

Alnahdi

Albalawi

2023

Sensors

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This study aims to address the challenges of managing the vast amount of data generated by Internet of Things (IoT) devices by categorizing stakeholders based on their roles in IoT security. As the number of connected devices increases, so do the associated security risks, highlighting the need for skilled stakeholders to mitigate these risks and prevent potential attacks. The study proposes a two-part approach, which involves clustering stakeholders according to their responsibilities and identifying relevant features. The main contribution of this research lies in enhancing decision-making processes within IoT security management. The proposed stakeholder categorization provides valuable insights into the diverse roles and responsibilities of stakeholders in IoT ecosystems, enabling a better understanding of their interrelationships. This categorization facilitates more effective decision making by considering the specific context and responsibilities of each stakeholder group. Additionally, the study introduces the concept of weighted decision making, incorporating factors such as role and importance. This approach enhances the decision-making process, enabling stakeholders to make more informed and context-aware decisions in the realm of IoT security management. The insights gained from this research have far-reaching implications. Not only will they benefit stakeholders involved in IoT security, but they will also assist policymakers and regulators in developing effective strategies to address the evolving challenges of IoT security.

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“…With the recent advances in Internet of Things (IoT) technologies, there is an increasing need to develop nextgeneration electronic devices that are lightweight, 1,2 portable, 3,4 and wearable. 5,6 Combined with artificial intelligence technologies, the smart sensors for IoT platforms are advancing from the lock-and-key type that monitors a single stimulus to the multiplex type that detects complex stimuli with high precision.…”

Section: Introductionmentioning

confidence: 99%

“…With the recent advances in Internet of Things (IoT) technologies, there is an increasing need to develop next-generation electronic devices that are lightweight, , portable, , and wearable. , Combined with artificial intelligence technologies, the smart sensors for IoT platforms are advancing from the lock-and-key type that monitors a single stimulus to the multiplex type that detects complex stimuli with high precision. , With this technological advancement, demand is growing for key sensing materials that can collect information (e.g., temperature, , humidity, , atmosphere, water quality, light, and movement) from surrounding environmental media.…”

Section: Introductionmentioning

confidence: 99%

Recent Advances in Smart Organic Sensors for Environmental Monitoring Systems

Song

Choi

Jeon

et al. 2022

ACS Appl. Electron. Mater.

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The recent emergence of the Internet of Things (IoT) and the rise of artificial intelligence have led to substantial interest in gathering information through various sensing techniques. In particular, environmental sensing technologies implemented through the IoT have important roles in automated smart platforms. In this review, we introduce research trends and prospects for sensing technologies based on organic materials that collect information from surrounding environmental media. State-of-the-art chemical, optical, and physical organic sensors hold great promise for the realization of smart environmental systems combined with IoT network platforms.

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A Review on IoT Operating Systems

Cited by 4 publications

References 0 publications

Linux IoT Malware Variant Classification Using Binary Lifting and Opcode Entropy

Linux IoT Malware Variant Classification Using Binary Lifting and Opcode Entropy

Role-Driven Clustering of Stakeholders: A Study of IoT Security Improvement

Recent Advances in Smart Organic Sensors for Environmental Monitoring Systems

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