Survey of Machine Learning based intrusion detection methods for Internet of Medical Things

Si-Ahmed, Ayoub; Al-Garadi, Mohammed Ali; Narhimene, Boustia

doi:10.1016/j.asoc.2023.110227

Cited by 43 publications

(22 citation statements)

References 43 publications

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“…The link genome contains the input node ID, the output node ID, the link weight, the bit (presence/absence of link), and the update number. The node genome contains the node ID, node type, and function type [26], [27], [28]. The HyperNEAT method is an extended version of NEAT that uses multidimensional geometric structures.…”

Section: A Neuro-evolutionary Algorithm For Detecting Networkmentioning

confidence: 99%

Network Attack Detection Using NeuroEvolution of Augmenting Topologies (NEAT) Algorithm

Zhukabayeva,

Adamova,

Ven-Tsen

et al. 2024

JOIV : Int. J. Inform. Visualization

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The imperfection of existing intrusion detection methods and the changing nature of malicious actions on the attacker's part led to the Internet of Things (IoT) network interaction in an unsafe state. The actual problem of improving the technology of the IOT is counteracting malicious network impacts. In this regard, research and development aimed at creating effective tools for solving applied problems within the framework of this problem are becoming increasingly important. This study seeks to develop tools for detecting anomalous network conditions resulting from malicious attacks. In particular, the accuracy of the identification of DoS and DDoS attacks is sufficient for operational use. This study analyzes various multi-level architectures, relevant communication protocols, and different types of network attacks. The presented research was conducted on open datasets TON_IOT DATASETS, which include multiple data sources collected from IoT sensors. The modified HyperNEAT algorithm was used as the basis for the development. The NEAT methodology used in the study allows you to combine various network nodes. Results of the study: a neuro-evolutionary algorithm for identifying DoS and DDoS attacks was implemented, integrated, and real-tested based on a multi-level analysis of network traffic combined with various adaptive modules. The accuracy of identifying DoS and DDoS attacks is 0.9242 in the Accuracy metric. The study implies that the proposed approach can be recommended for network intrusion detection, ensuring security when interacting with the IoT.

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Section: A Neuro-evolutionary Algorithm For Detecting Networkmentioning

confidence: 99%

Network Attack Detection Using NeuroEvolution of Augmenting Topologies (NEAT) Algorithm

Zhukabayeva,

Adamova,

Ven-Tsen

et al. 2024

JOIV : Int. J. Inform. Visualization

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“…Intrusion detection systems for H-IoT are described in this section [240]. An intrusion typically targets a network or device's integrity, availability, or confidentiality by impairing its security.…”

Section: Intrusion Detection System In Healthcare-iotmentioning

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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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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“…This can include the use of secure communication standards, such as a secure socket layer (SSL) and transport layer security (TLS), to ensure that data are transmitted securely and cannot be tampered with or intercepted [ 20 , 21 , 22 ]. Another solution is to improve the security of the devices themselves, for example, by implementing secure boot mechanisms to prevent malware infections and ensuring that software updates are securely transmitted and installed [ 23 , 24 ]. In addition, researchers have proposed the use of machine learning and deep learning algorithms and intrusion detection systems to detect and respond to cyber threats in real time [ 25 ].…”

Section: Introductionmentioning

confidence: 99%

“…Deep learning algorithms can be used to model this normal behavior and detect anomalies in real time. In the field of IoMT security, deep learning algorithms can also be used to perform automated feature selection, which can help to reduce the risk of overfitting and improve the performance of intrusion detection systems [ 23 , 37 ]. Additionally, deep learning algorithms can be used to generate representations of the data that can be used as inputs for intrusion detection models, allowing these models to learn more effective representations of the data and improve their accuracy in detecting cyber threats [ 38 ].…”

Section: Introductionmentioning

confidence: 99%

An Improved Mutual Information Feature Selection Technique for Intrusion Detection Systems in the Internet of Medical Things

Alalhareth,

Hong

2023

Sensors

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In healthcare, the Internet of Things (IoT) is used to remotely monitor patients and provide real-time diagnoses, which is referred to as the Internet of Medical Things (IoMT). This integration poses a risk from cybersecurity threats that can harm patient data and well-being. Hackers can manipulate biometric data from biosensors or disrupt the IoMT system, which is a major concern. To address this issue, intrusion detection systems (IDS) have been proposed, particularly using deep learning algorithms. However, developing IDS for IoMT is challenging due to high data dimensionality leading to model overfitting and degraded detection accuracy. Feature selection has been proposed to prevent overfitting, but the existing methods assume that feature redundancy increases linearly with the size of the selected features. Such an assumption does not hold, as the amount of information a feature carries about the attack pattern varies from feature to feature, especially when dealing with early patterns, due to data sparsity that makes it difficult to perceive the common characteristics of selected features. This negatively affects the ability of the mutual information feature selection (MIFS) goal function to estimate the redundancy coefficient accurately. To overcome this issue, this paper proposes an enhanced feature selection technique called Logistic Redundancy Coefficient Gradual Upweighting MIFS (LRGU-MIFS) that evaluates candidate features individually instead of comparing them with common characteristics of the already-selected features. Unlike the existing feature selection techniques, LRGU calculates the redundancy score of a feature using the logistic function. It increases the redundancy value based on the logistic curve, which reflects the nonlinearity of the relationship of the mutual information between features in the selected set. Then, the LRGU was incorporated into the goal function of MIFS as a redundancy coefficient. The experimental evaluation shows that the proposed LRGU was able to identify a compact set of significant features that outperformed those selected by the existing techniques. The proposed technique overcomes the challenge of perceiving common characteristics in cases of insufficient attack patterns and outperforms existing techniques in identifying significant features.

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Survey of Machine Learning based intrusion detection methods for Internet of Medical Things

Cited by 43 publications

References 43 publications

Network Attack Detection Using NeuroEvolution of Augmenting Topologies (NEAT) Algorithm

Network Attack Detection Using NeuroEvolution of Augmenting Topologies (NEAT) Algorithm

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

An Improved Mutual Information Feature Selection Technique for Intrusion Detection Systems in the Internet of Medical Things

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