A Review on Machine Learning-based Malware Detection Techniques for Internet of Things (IoT) Environments

Sasikala, S.; Janakiraman, Sengathir

doi:10.1007/s11277-023-10693-w

Cited by 4 publications

(2 citation statements)

References 47 publications

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“…Similarly, the graph shows the evaluation metrics for all the patients in the dataset. The graph helps to visualize and compare the different evaluation metrics across the different patients [25] [26], thereby giving insights into the performance of the MECC system.…”

Section: Results and Analysismentioning

confidence: 99%

Optimizing Resource Allocation in MECC for AI and DL Applications in Healthcare with Task Offloading

Sree,

Prathyusha,

Rao

2023

IJCERT

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The efficient management of emergency medical services in Medical Emergency Command Centers (MECC) is critical, and optimizing resource allocation is a key aspect of this management. However, with the increasing use of Artificial Intelligence (AI) and Deep Learning (DL) applications in healthcare, optimizing resource allocation has become more challenging. To address this challenge, we propose a task offloading-based approach that involves distributing computational tasks across different resources in a network to optimize resource utilization. Our approach involves analyzing the MECC network topology to identify available computing resources such as edge devices, cloud servers, and data centers. We then develop a task offloading strategy that determines which tasks should be offloaded to which resources based on computational requirements, network latency, and resource availability. Additionally, we implement a resource allocation algorithm that allocates resources to tasks based on their priority, resource availability, and current workload. We continuously monitor the system's performance and fine-tune the resource allocation algorithm to optimize resource utilization and reduce response time. Our experimental results demonstrate that our approach can significantly improve the efficiency of resource allocation in MECC for AI and DL applications, resulting in faster response times and better patient care.

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Section: Results and Analysismentioning

confidence: 99%

Optimizing Resource Allocation in MECC for AI and DL Applications in Healthcare with Task Offloading

Sree,

Prathyusha,

Rao

2023

IJCERT

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“…Regular firmware updates are essential to address emerging security vulnerabilities, and automated patch management systems ensure these updates are consistently applied. Furthermore, the encryption of data, both in transit and at rest, coupled with data integrity checks, safeguards sensitive information against interception and tampering [12]. However, technical solutions alone are not sufficient.…”

Section: Prevention and Mitiga-tion Strategiesmentioning

confidence: 99%

IoT Malware: A Comprehensive Survey of Threats, Vulnerabilities, and Mitigation Strategies

Muhammad Shairoze Malik

2024

IJECI

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The proliferation of the Internet of Things (IoT) has ushered in a new era of connectivity and convenience, linking a vast array of devices from household appliances to industrial machinery. However, this interconnectivity also introduces significant security vulnerabilities, making IoT systems attractive targets for malicious actors. This comprehensive survey delves into the multifaceted world of IoT malware, exploring the evolving landscape of threats that plague these systems. We methodically analyze various types of IoT malware, identifying common attack vectors and the intrinsic vulnerabilities that IoT devices often possess. These vulnerabilities range from inadequate security protocols to the use of default credentials and unpatched software. Furthermore, the paper highlights real-world instances where IoT devices have been compromised, leading to significant disruptions and breaches of privacy. In addressing these challenges, we outline an array of mitigation strategies. These strategies include but are not limited to, enhanced encryption methods, regular firmware updates, network segmentation, and the adoption of robust authentication mechanisms. We also discuss the role of machine learning and artificial intelligence in predicting and preventing IoT malware attacks. Moreover, our survey extends to the regulatory and ethical considerations surrounding IoT security, advocating for a more proactive approach in standard-setting and compliance enforcement. The findings of this study aim to serve as a foundational resource for researchers, cybersecurity professionals, and policymakers, emphasizing the need for a collective and informed effort in fortifying the IoT ecosystem against the ever-growing threat of malware.

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Advancing Android Malware Detection with BioSentinel Neural Network using Hybrid Deep Learning Techniques

Rani,

Gnaneshwar,

Sampurnima Pattem

et al. 2024

2024 11th International Conference on Computing for Sustainable Global Development (INDIACom)

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A Review on Machine Learning-based Malware Detection Techniques for Internet of Things (IoT) Environments

Cited by 4 publications

References 47 publications

Optimizing Resource Allocation in MECC for AI and DL Applications in Healthcare with Task Offloading

Optimizing Resource Allocation in MECC for AI and DL Applications in Healthcare with Task Offloading

IoT Malware: A Comprehensive Survey of Threats, Vulnerabilities, and Mitigation Strategies

Advancing Android Malware Detection with BioSentinel Neural Network using Hybrid Deep Learning Techniques

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