A Novel Framework for Threat Analysis of Machine Learning-based Smart Healthcare Systems

Haque, Nur Imtiazul; Rahman, Mohammad Ashiqur; Shahriar, Md Hasan; Khalil, Alvi Ataur; Uluagac, Selcuk

doi:10.48550/arxiv.2103.03472

Cited by 5 publications

(6 citation statements)

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“…The work considers threat intelligence and predicts the Tactics, Techniques, and Procedures (TTP) deployed for a cyber attack, demonstrating high accuracy in their experimental assessment. Another novel threat analysis framework was proposed by [ 31 ], SHChecker, combines ML and formal analysis capabilities for the Smart Healthcare Systems (SHSs). In detail, the paper focuses on Internet of Medical Things (IoMT) and adopts several ML algorithms, including Decision Tree (DT), Artificial Neural Network (ANN), K-means, and others.…”

Section: Related Workmentioning

confidence: 99%

A Machine Learning Approach for the NLP-Based Analysis of Cyber Threats and Vulnerabilities of the Healthcare Ecosystem

Silvestri

Islam

Tzagkarakis

et al. 2023

Sensors

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Digitization in healthcare systems, with the wid adoption of Electronic Health Records, connected medical devices, software and systems providing efficient healthcare service delivery and management. On the other hand, the use of these systems has significantly increased cyber threats in the healthcare sector. Vulnerabilities in the existing and legacy systems are one of the key causes for the threats and related risks. Understanding and addressing the threats from the connected medical devices and other parts of the ICT health infrastructure are of paramount importance for ensuring security within the overall healthcare ecosystem. Threat and vulnerability analysis provides an effective way to lower the impact of risks relating to the existing vulnerabilities. However, this is a challenging task due to the availability of massive data which makes it difficult to identify potential patterns of security issues. This paper contributes towards an effective threats and vulnerabilities analysis by adopting Machine Learning models, such as the BERT neural language model and XGBoost, to extract updated information from the Natural Language documents largely available on the web, evaluating at the same time the level of the identified threats and vulnerabilities that can impact on the healthcare system, providing the required information for the most appropriate management of the risk. Experiments were performed based on CS news extracted from the Hacker News website and on Common Vulnerabilities and Exposures (CVE) vulnerability reports. The results demonstrate the effectiveness of the proposed approach, which provides a realistic manner to assess the threats and vulnerabilities from Natural Language texts, allowing adopting it in real-world Healthcare ecosystems.

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Section: Related Workmentioning

confidence: 99%

A Machine Learning Approach for the NLP-Based Analysis of Cyber Threats and Vulnerabilities of the Healthcare Ecosystem

Silvestri

Islam

Tzagkarakis

et al. 2023

Sensors

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“…Haque et al [33] Smart healthcare systems (SHSs) provide quick and effective treatment of diseases using the Internet for Medical Substances (IOS) based wireless body sensor networks (WBSNs) and implanted devices (IMDs). Adversaries may, nonetheless, conduct numerous assaults on the communicational network and the hardware/firmware to introduce fake data or prevent the automated medication system from being available to threaten the patient's life.…”

Section: Literature Surveymentioning

confidence: 99%

Fuzzy Decision-Making Method based Evaluation of Smart Healthcare Management

Quasim

Shaikh

Shuaib

et al. 2023

Preprint

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The smart medical system is becoming a health policy service that employs wearables, online services and mobile devices to connect to the internet continuously and connect patients, technology and healthcare centers, and then cope efficiently and thoughtfully with the demands of medical ecosystems. This article explores some of the issues facing users to speed up using and accepting intelligent medical technology for access to omnipresent healthcare. The article analyses how Fuzzy Decision Making may be integrated to create better health solutions with an intelligent approach. The smart medical care management system enables the patient to use medical services and services in any place and at any time, including emergency preparedness, medication management and monitoring services. An assessment of this method for managing new concepts comparable to direct health services must be examined. Hence, this paper Smart Healthcare Management Evaluation utilizing Fuzzy Decision Making (SHME-FDM) model, has been proposed to evaluate the technological integration performance. The present paper assesses the security of health data privacy in the intelligent medicare system utilizing the Fuzzy Analytical Hierarchy Process- Preference Technique similar to the ideal solution (Fuzzy AHP-TOPSIS). This paper usage the fuzzy neural network for health care prediction. The test analyses the reliability, error rate accuracy of the fuzzy outcomes. The security hazard analysis data demonstrate that the suggested fuzzy model has the highest risk assessment performance in contrast to the current models.

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“…Various machine-and deep-learning approaches have been proposed for the prediction of security threats on multiple datasets: TON-IOT [30][31][32], UNSW-NB15 [33], intrusion detection dataset [34][35][36], etc. However, these approaches lack performance and are limited in that they do not consider multi-label attack datasets [37]; malicious activities such as DDoS and ransomware, and removal of redundant and irrelevant features [31]; feature selection techniques for feature optimization [31]; or low accuracy [34,38].…”

Section: Introductionmentioning

confidence: 99%

Harris-Hawk-Optimization-Based Deep Recurrent Neural Network for Securing the Internet of Medical Things

Abbas,

Sampedro,

Abisado

et al. 2023

Electronics

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The healthcare industry has recently shown much interest in the Internet of Things (IoT). The Internet of Medical Things (IoMT) is a component of the IoTs in which medical appliances transmit information to communicate critical information. The growth of the IoMT has been facilitated by the inclusion of medical equipment in the IoT. These developments enable the healthcare sector to interact with and care for its patients effectively. Every technology that relies on the IoT can have a serious security challenge. Critical IoT connectivity data may be exposed, changed, or even made unavailable to authenticated users in the case of such attacks. Consequently, protecting IoT/IoMT systems from cyber-attacks has become essential. Thus, this paper proposes a machine-learning- and a deep-learning-based approach to creating an effective model in the IoMT system to classify and predict unforeseen cyber-attacks/threats. First, the dataset is preprocessed efficiently, and the Harris Hawk Optimization (HHO) algorithm is employed to select the optimized feature. Finally, machine learning and deep learning algorithms are applied to detect cyber-attack in IoMT. Results reveal that the proposed approach achieved an accuracy of 99.85%, outperforming other techniques and existing studies.

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A Novel Framework for Threat Analysis of Machine Learning-based Smart Healthcare Systems

Cited by 5 publications

References 0 publications

A Machine Learning Approach for the NLP-Based Analysis of Cyber Threats and Vulnerabilities of the Healthcare Ecosystem

A Machine Learning Approach for the NLP-Based Analysis of Cyber Threats and Vulnerabilities of the Healthcare Ecosystem

Fuzzy Decision-Making Method based Evaluation of Smart Healthcare Management

Harris-Hawk-Optimization-Based Deep Recurrent Neural Network for Securing the Internet of Medical Things

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