Security Threat Management by Software Obfuscation for Privacy in Internet of Medical Thing (IoMT) Application

Kavitha, D.; Subramaniam, Chandrasekaran

doi:10.1166/jctn.2017.6602

Cited by 10 publications

(4 citation statements)

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“…Throughout history, cyber physical attacks have taken advantage of weaknesses that are characteristics rather than defects, ranging from known instances of programmes in the 1960s that could exercise disc weapons on hard drives and cause them to respond to the Stuxnet assault, which looked to be aimed at atomic enriching enriched uranium, by self-destructing in 2007-2010. (and allegedly succeeded) [7] .…”

Section: Internet Of Things Risksmentioning

confidence: 95%

The Prospective and Internal Risks of the Internet

Gupta¹

2021

SJRIT

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Section: Internet Of Things Risksmentioning

confidence: 95%

The Prospective and Internal Risks of the Internet

Gupta¹

2021

SJRIT

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“…Besides, some solutions ensure the medications and treatments are used as medical doctors recommend and are also referred to as medication reminders [43,44]. In all these cases, IoMT offers a simpler approach to remote medicine and alert systems by integrating devices and software platforms [45,46].…”

Section: Internet Of Medical Things (Iomt) In Healthcarementioning

confidence: 99%

CICIoMT2024: Attack Vectors in Healthcare devices-A Multi-Protocol Dataset for Assessing IoMT Device Security

Dadkhah,

Carlos Pinto Neto,

Ferreira

et al. 2024

Preprint

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The Internet of Things (IoT) has a growing presence in society's daily lives. These lightweight devices can be easily deployed and maintained, enabling extensive adoption in different environments. Furthermore, one of the most promising areas for using IoT devices is healthcare, comprising devices referred to as the Internet of Medical Things (IoMT). Several examples of healthcare services are supported by IoMT devices, e.g., continuous health monitoring. Conversely, there is an increasing concern with the cybersecurity aspects of these devices, and several attacks against IoT infrastructures have been launched in the past few years. These cybersecurity concerns also apply to healthcare applications, where the tradeoff between the benefits and security of IoMT devices must be observed. Given the complexity and amount of data IoMT network traffic generates, advanced methods become especially useful in these environments. Although Machine Learning (ML) brings various techniques and solutions to improve cyberattack detection, prevention, and mitigation, essential features are not addressed in the current state-of-the-art benchmark dataset contributions. Thereupon, the main goal of this research is to propose a realistic benchmark dataset to enable the development and evaluation of IoMT security solutions. In order to accomplish this, 18 attacks were executed against an IoMT testbed composed of 40 IoMT devices (25 real devices and 15 simulated devices), considering the plurality of protocols used in healthcare (e.g., Wi-Fi, MQTT, and Bluetooth). These attacks are categorized into five classes: DDoS, DoS, Recon, MQTT, and spoofing. This effort aims to establish a baseline complementary to the state-of-the-art contributions. The outcome supports researchers in investigating and developing new solutions to make healthcare systems more secure using different mechanisms (e.g., machine learning - ML). This research goes beyond merely conducting attacks on IoMT devices. We also attempt to capture the lifecycle of these devices in different vital phases, from the moment they join the network until they leave, which is called profiling. Profiling allows the different classifiers to identify anomalies of each device individually in the healthcare network. The CICIoMT2024 dataset has been published on CIC's dataset page, making it available for other researchers to use.

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“…The cryptography approach safeguards data privacy by obfuscating medical applications to produce computationally indistinguishable outputs. Kavitha et al [69] established a formal model for addressing security concerns using the program obfuscation approach. Their indistinguishable inscrutable obfuscated medical data transfer can be deployed between standard-compliant equipment in a health service center or clinical center to eliminate fraud and internal human risks.…”

Section: ) Authentication Based On Cryptographymentioning

confidence: 99%

A Survey: To Govern, Protect, and Detect Security Principles on Internet of Medical Things (IoMT)

Alhaj

Abdulla²,

Iderss³

et al. 2022

IEEE Access

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The integration of medical equipment into the Internet of Things (IoT) led to the introduction of Internet of Medical Things (IoMT). Variation of IoT devices have been equipped in medical facilities. These devices provided convenience to healthcare provider since they can continuously monitor their patients in real-time, while allowing them to have greater physical flexibility and mobility. However, users of healthcare services (such as patients and medical staff) often are less concerned about security issues associated with IoT. These alleviate existing problems and jeopardize the lives of their patients by making them susceptible to attacks. Furthermore, IoMT applications have direct access to healthcare services because it handles sensitive patient information. Therefore, it is extremely important to preserve and establish the security and privacy of IoMT. This further justifies the need to investigate and address the related issues. Despite existing literature on security and privacy mechanisms, the domain still requires more attention. Therefore, this paper aims to discuss the security and privacy principles, as well as challenges associated with IoMT. Besides, a comprehensive analysis of privacy and security solutions for IoMT is also presented. In addition, we introduced a novel taxonomy of IoMT security and privacy based on cyber security principles such as "govern," "protect," and "detect". In conclusion, this paper provides a discussion on existing challenges and future direction for researchers.

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Security Threat Management by Software Obfuscation for Privacy in Internet of Medical Thing (IoMT) Application

Cited by 10 publications

References 0 publications

The Prospective and Internal Risks of the Internet

The Prospective and Internal Risks of the Internet

CICIoMT2024: Attack Vectors in Healthcare devices-A Multi-Protocol Dataset for Assessing IoMT Device Security

A Survey: To Govern, Protect, and Detect Security Principles on Internet of Medical Things (IoMT)

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