Anomaly detection in Internet of medical Things with Blockchain from the perspective of deep neural network

Wang, Jun; Jin, Hanlei; Chen, Junxiao; Tan, Jinghua; Zhong, Kaiyang

doi:10.1016/j.ins.2022.10.060

Cited by 23 publications

(5 citation statements)

References 43 publications

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“…With 5G networks facilitating rapid, high-volume data exchanges, the IoMT has gained momentum in the healthcare sector. In [260], the emphasis is on detecting anomalies using deep learning for enhanced security within 5G contexts in IoM. By utilizing multi-model autoencoders for feature extraction, the complexity of traffic feature information has been significantly reduced.…”

Section: A Anomaly Detectionmentioning

confidence: 99%

“…With 5G networks facilitating rapid, voluminous data exchanges, the Internet of Medical Things (IoMT) has gained momentum in the healthcare sector. In [257], the emphasis is on detecting anomalies using deep learning for enhanced security within 5G contexts in IoM. By utilizing multi-model autoencoders for feature extraction, the complexity of traffic an up-to-date antivirus tool to detect and protect the end-point for new vulnerabilities, (vi) the human element is the weakest link and its essential to build cyber awareness within the workplace environment and train them so that they are cyber prepared, (vii) enabling application security using vulnera-bility scanners and making sure that only inventoried tools are used, (viii) last but not the least, to ensure data security mechanisms are in place (i.e.…”

Section: A Anomaly Detectionmentioning

confidence: 99%

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Machine Learning for Healthcare-IoT Security: A Review and Risk Mitigation

Khatun,

Memon,

Eising

et al. 2023

IEEE Access

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The Healthcare Internet-of-Things (H-IoT), commonly known as Digital Healthcare, is a datadriven infrastructure that highly relies on smart sensing devices (i.e., blood pressure monitors, temperature sensors, etc.) for faster response time, treatments, and diagnosis. However, with the evolving cyber threat landscape, IoT devices have become more vulnerable to the broader risk surface (e.g., risks associated with generative AI, 5G-IoT, etc.), which, if exploited, may lead to data breaches, unauthorized access, and lack of command and control and potential harm. This paper reviews the fundamentals of healthcare IoT, its privacy, and data security challenges associated with machine learning and H-IoT devices. The paper further emphasizes the importance of monitoring healthcare IoT layers such as perception, network, cloud, and application. Detecting and responding to anomalies involves various cyber-attacks and protocols such as Wi-Fi 6, Narrowband Internet of Things (NB-IoT), Bluetooth, ZigBee, LoRa, and 5G New Radio (5G NR). A robust authentication mechanism based on machine learning and deep learning techniques is required to protect and mitigate H-IoT devices from increasing cybersecurity vulnerabilities. Hence, in this review paper, security and privacy challenges and risk mitigation strategies for building resilience in H-IoT are explored and reported.

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Section: A Anomaly Detectionmentioning

confidence: 99%

Section: A Anomaly Detectionmentioning

confidence: 99%

Machine Learning for Healthcare-IoT Security: A Review and Risk Mitigation

Khatun,

Memon,

Eising

et al. 2023

IEEE Access

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“…Similarly, research in [361] proposes using Permissioned Blockchain-based Federated Learning for Anomaly Detection (PBFLAD), where changes to the AI framework are linked utilizing a shared ledger, allowing auditing of the machine learning models. Furthermore, in [362], IoMT Blockchain network Anomaly Detection (IoMTBC-AD) is employed to prevent insider attacks in blockchain networks utilized in IoMT by combining the network with deep learning to detect network anomalies. Moreover, Hybrid Deep Learning (HDL) making use of LSTM and convolutional neural networks for evaluating traffic flow anomalies by assisting blockchain in resolving gaps in the datasets has been studied in [363].…”

Section: Anomaly or Intrusion Diagnosis And Suppressionmentioning

confidence: 99%

“…BFT-IoMT [358] Anomaly detection DAG blockchain [359], Brain-chain [360], PBFLAD [361], IoMTBC-AD [362], HDL [363] Virtual network ----BTNV [365], DQL-KDVN [366], DLT [367], CBDST [368], SliceBlock [369], BNSB [370], Skunk [371], SRT-DDQ [372], TTRAS [373] Big data analysis ----BlockIoTIntelligence [374], DFL-B [375] Cloud/edge compu.…”

Section: Packet Forwardingmentioning

confidence: 99%

A Review of Blockchain Technology in Knowledge-Defined Networking, Its Application, Benefits, and Challenges

Wijesekara,

Gunawardena

2023

Network

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Knowledge-Defined Networking (KDN) necessarily consists of a knowledge plane for the generation of knowledge, typically using machine learning techniques, and the dissemination of knowledge, in order to make knowledge-driven intelligent network decisions. In one way, KDN can be recognized as knowledge-driven Software-Defined Networking (SDN), having additional management and knowledge planes. On the other hand, KDN encapsulates all knowledge-/intelligence-/ cognition-/machine learning-driven networks, emphasizing knowledge generation (KG) and dissemination for making intelligent network decisions, unlike SDN, which emphasizes logical decoupling of the control plane. Blockchain is a technology created for secure and trustworthy decentralized transaction storage and management using a sequence of immutable and linked transactions. The decision-making trustworthiness of a KDN system is reliant on the trustworthiness of the data, knowledge, and AI model sharing. To this point, a KDN may make use of the capabilities of the blockchain system for trustworthy data, knowledge, and machine learning model sharing, as blockchain transactions prevent repudiation and are immutable, pseudo-anonymous, optionally encrypted, reliable, access-controlled, and untampered, to protect the sensitivity, integrity, and legitimacy of sharing entities. Furthermore, blockchain has been integrated with knowledge-based networks for traffic optimization, resource sharing, network administration, access control, protecting privacy, traffic filtering, anomaly or intrusion detection, network virtualization, massive data analysis, edge and cloud computing, and data center networking. Despite the fact that many academics have employed the concept of blockchain in cognitive networks to achieve various objectives, we can also identify challenges such as high energy consumption, scalability issues, difficulty processing big data, etc. that act as barriers for integrating the two concepts together. Academicians have not yet reviewed blockchain-based network solutions in diverse application categories for diverse knowledge-defined networks in general, which consider knowledge generation and dissemination using various techniques such as machine learning, fuzzy logic, and meta-heuristics. Therefore, this article fills a void in the content of the literature by first reviewing the diverse existing blockchain-based applications in diverse knowledge-based networks, analyzing and comparing the existing works, describing the advantages and difficulties of using blockchain systems in KDN, and, finally, providing propositions based on identified challenges and then presenting prospects for the future.

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“…A method to detect attack traffic using a deep neural network in the IoMT-Blockchain environment is proposed in [6]. The study used a multi-model autoencoder (MMAE) to effectively learn the fusion of low-dimensional feature representations between different features from the original data.…”

Section: Related Workmentioning

confidence: 99%

Anomaly Detection for the Internet-of-Medical-Things

Reji,

Pranggono,

Marchang

et al. 2023

2023 IEEE International Conference on Communications Workshops (ICC Workshops)

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Cybersecurity for the Internet of Medical Things (IoMT) is a very concerning issue because of emerging cyber threats and security incidents targeting IoMT devices all over the world. The healthcare system has near-zero tolerance for inexplicability. In this paper, we propose a machine learningbased anomaly detection for the IoMT and evaluate the performance using a realistic public dataset. We implement various machine learning algorithms: Random Forest, Decision Tree, Logistic Regression, Support Vector Machine, and K-Nearest Neighbor with TON IoT dataset. Two types of classifications are implemented: binary and categorical. In the categorical classification, evaluation for nine attack scenarios (Scanning, DoS, password cracking attack, and Man-in-The-Middle (MITM)) are performed. The test results demonstrate that Support Vector Machine models produce better performance compared to the other models.

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Anomaly detection in Internet of medical Things with Blockchain from the perspective of deep neural network

Cited by 23 publications

References 43 publications

Machine Learning for Healthcare-IoT Security: A Review and Risk Mitigation

Machine Learning for Healthcare-IoT Security: A Review and Risk Mitigation

A Review of Blockchain Technology in Knowledge-Defined Networking, Its Application, Benefits, and Challenges

Anomaly Detection for the Internet-of-Medical-Things

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