Trustworthy Intrusion Detection in E-Healthcare Systems

Akram, Faiza; Li, Dongsheng; Zhao, Peibiao; Kryvinska, Natalia; Abbas, Sidra; Rizwan, Muhammad

doi:10.3389/fpubh.2021.788347

Cited by 11 publications

(7 citation statements)

References 46 publications

(45 reference statements)

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“…Trustworthy intrusion detection model in e-healthcare systems 40 is a security tool for detecting malicious network traffic and helping to maintain patient health records safely. It uses an adaptive neuro-fuzzy inference system (ANFIS) to detect the unauthorized access of users.…”

Section: Resultsmentioning

confidence: 99%

Internet of Medical Things Security Frameworks for Risk Assessment and Management: A Scoping Review

Svandova,

Smutny

2024

JMDH

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Background The massive expansion of the Internet of medical things (IoMT) technology brings many opportunities for improving healthcare. At the same time, their use increases security risks, brings security and privacy concerns, and threatens the functioning of healthcare facilities or healthcare provision. Purpose This scoping review aims to identify progress in designing risk assessment and management frameworks for IoMT security. The frameworks found are divided into two groups according to whether frameworks address the technological design of risk management or assess technological measures to ensure the security of the IoMT environment. Furthermore, the article intends to find out whether frameworks also include an assessment of organisational measures related to IoMT security. Methods This review was prepared using PRISMA ScR guidelines. Relevant studies were searched in the citation databases Web of Science and Scopus. The search was limited to articles published in English between 2018 and 17 September 2023. The initial search yielded 1341 articles, of which 44 (3.3%) were included in the scoping review. A qualitative content analysis focused on selected security perspectives and progress in the given area was carried out. Results Thirty-two articles describe the design of risk assessment and management frameworks. Twelve articles describe the design of frameworks for assessing the security of IoMT devices and possibly offer a comparison of different IoMT alternatives. A description of the included articles was prepared from the selected security perspectives. Conclusion The review shows the need to create comprehensive or holistic frameworks for operational security and privacy risk management at all layers of the IoMT architecture. It includes the design of specific technological solutions and frameworks for continuously assessing the overall level of information security and privacy of the IoMT environment. Unfortunately, none of the found frameworks offer an assessment of organizational measures even though the importance of the organization measures was highlighted in articles. Another area of interest for researchers could be the design of a general risk management database for IoMT, which would include potential IoMT-related risks connected to a particular device.

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Section: Resultsmentioning

confidence: 99%

Internet of Medical Things Security Frameworks for Risk Assessment and Management: A Scoping Review

Svandova,

Smutny

2024

JMDH

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“…In this case, the dataset dictates the collection location. Examples are: Akram et al [36] and Begli et al [139]. Others combine the toolboxes with different simulators or platforms.…”

Section: B Rq 2 -Data Collection and Processing Locationsmentioning

confidence: 99%

Attack Detection for Medical Cyber-Physical Systems–A Systematic Literature Review

et al. 2023

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The threat situation due to cyber attacks in hospitals is emerging and patient life is at risk. One significant source of potential vulnerabilities is medical cyber-physical systems (MCPS). Detecting intrusions in this environment faces challenges different from other domains, mainly due to the heterogeneity of devices, the diversity of connectivity types, and the variety of terminology. To summarize existing results, we conducted a structured literature review (SLR) following the guidelines of Kitchenham et al. for SLRs in software engineering. We developed six research questions regarding detection approach, detection location, included features, adversarial focus, utilized datasets, and intrusion prevention. We identified that most researchers focused on an anomaly-based detection approach at the network layer. The primary focus was on the detection of malicious insiders. While several researchers used publicly available datasets for training and testing their algorithms, the lack of suitable datasets resulted in the development of testbeds consisting of various medical devices. Based on the results, we formulated five future research topics. First, the special conditions of hospital networks, the MCPS deployed within them, and the contrasts to other IT and OT environments should be examined. Thereupon, MCPS-specific datasets should be created that allow researchers to address the health domain's unique requirements and possibilities. At the same time, endeavors aimed at standardization in this area should be supported and expanded. Moreover, the use of medical context for attack detection should be further explored. Last but not least, efforts for MCPS-tailored intrusion prevention should be intensified. This way, the emerging threat landscape can be addressed, IT security in hospitals can be improved, and patient health can be protected.

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“…As medical technology advances, the usage of crucial medical data for monitoring, prevention, and diagnosis of infectious tumors upsurges. This process usually depends upon managing, collecting, and analyzing the data set for infectious tumor diagnosis and treatment [2,3,4]. The timely detection of a brain tumor can have a positive impact on the patient's chances of survival and quality of life.…”

Section: Introductionmentioning

confidence: 99%

Intracranial Tumor Detection using Magnetic Resonance Imaging and Deep Learning

Syed,

Khan,

Ahmed

et al. 2023

IJEMD-CSAI

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An intracranial tumor is a malignant Central Nervous System (CNS) cancer that significantly contributes to global mortality. Timely prediction of these brain tumors can improve the survival rates of a patient. Magnetic Resonance Imagining (MRI) and Computed Tomography (CT) have emerged as effective non-invasive ways to extract 2D or 3D images of human internal organs, eliminating any pain or surgical procedures. However, analyzing and distinguishing the normal and abnormal tissue is a challenging task. Due to this implying Datadriven approaches, could be a pragmatic way to efficiently classify and detect regions of malignancy of a tumor. The scope of this study is to efficiently predict Brain tumors and their location by employing sophisticated optimized Deep learning models like Convolutional neural Networks (CNN) and Long-Short Term Memory (LSTM) through limited MRI images dataset of human brain. The dataset acquired from kaggle is comprised of 253 MRI images covering different angles of the human brain. The images were of different sizes and shapes, to standardize them data preprocessing simulations were made including resizing, normalizing, etc. For better categorization of model, the images were also converted to binary format (0, 1) by the One Hot Encoding technique. The ratio of training and testing data was taken as 90:10. For CNN and LSTM model, suitable hyperparameters were selected through the trial-and-error technique to ensure the best optimization of the model on the implied training data. The loss and accuracy graph depicts optimized validation and accuracy losses indicating the model to call back early stopping to save computational cost. The predicted labels for both the models and actual label were compared by a confusion matrix which showed accuracy, specificity, recall, and misclassification error to be 91.51, 92.85, 90.12, and 8.49 on average for CNN,while 95.54, 92.86, 94.2, and 5.805 for LSTM model, respectively.

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Trustworthy Intrusion Detection in E-Healthcare Systems

Cited by 11 publications

References 46 publications

Internet of Medical Things Security Frameworks for Risk Assessment and Management: A Scoping Review

Internet of Medical Things Security Frameworks for Risk Assessment and Management: A Scoping Review

Attack Detection for Medical Cyber-Physical Systems–A Systematic Literature Review

Intracranial Tumor Detection using Magnetic Resonance Imaging and Deep Learning

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