RETRACTED ARTICLE: IoT-based intrusion detection system for healthcare using RNNBiLSTM deep learning strategy with custom features

Jeyanthi, D. V.; Indrani, B.

doi:10.1007/s00500-023-08536-8

Cited by 6 publications

(1 citation statement)

References 25 publications

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“… Gavel, Raghuvanshi & Tiwari (2021) proposed a two-axis dimensionality reduction technique based on Kalman filtering and the salp swarm algorithm with a kernel-based extreme learning machine as a multiclass classifier for intrusion detection in IoT networks, which has high detection accuracy on both NSL-KDD and CICIDS2017 datasets. Jeyanthi & Indrani (2023) proposed the ACAAS algorithm, which is used to extract important features from the IoTID20 dataset and utilize a recurrent neural network with long short-term memory for attack identification. Harris et al (2020) emphasized the importance of optimal feature selection techniques to improve the classification performance of the algorithm and combined the particle swarm optimization (PSO) search algorithm with other search algorithms for feature selection and validated the results with the J48 classification algorithm, demonstrating that the PSO algorithm and the combination with other search algorithms improved the TPR and accuracy of J48 classification.…”

Section: Literature Reviewmentioning

confidence: 99%

A lightweight intrusion detection method for IoT based on deep learning and dynamic quantization

Wang,

Chen,

Yang

et al. 2023

PeerJ Computer Science

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Intrusion detection ensures that IoT can protect itself against malicious intrusions in extensive and intricate network traffic data. In recent years, deep learning has been extensively and effectively employed in IoT intrusion detection. However, the limited computing power and storage space of IoT devices restrict the feasibility of deploying resource-intensive intrusion detection systems on them. This article introduces the DL-BiLSTM lightweight IoT intrusion detection model. By combining deep neural networks (DNNs) and bidirectional long short-term memory networks (BiLSTMs), the model enables nonlinear and bidirectional long-distance feature extraction of complex network information. This capability allows the system to capture complex patterns and behaviors related to cyber-attacks, thus enhancing detection performance. To address the resource constraints of IoT devices, the model utilizes the incremental principal component analysis (IPCA) algorithm for feature dimensionality reduction. Additionally, dynamic quantization is employed to trim the specified cell structure of the model, thereby reducing the computational burden on IoT devices while preserving accurate detection capability. The experimental results on the benchmark datasets CIC IDS2017, N-BaIoT, and CICIoT2023 demonstrate that DL-BiLSTM surpasses traditional deep learning models and cutting-edge detection techniques in terms of detection performance, while maintaining a lower model complexity.

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Section: Literature Reviewmentioning

confidence: 99%

A lightweight intrusion detection method for IoT based on deep learning and dynamic quantization

Wang,

Chen,

Yang

et al. 2023

PeerJ Computer Science

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Federated Learning for Collaborative Cybersecurity of Distributed Healthcare

Boudko

2023

Lecture Notes in Computer Science

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Data breaches in healthcare: security mechanisms for attack mitigation

Nemec Zlatolas,

Welzer,

Lhotska

2024

Cluster Comput

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The digitalisation of healthcare has increased the risk of cyberattacks in this sector, targeting sensitive personal information. In this paper, we conduct a systematic review of existing solutions for data breach mitigation in healthcare, analysing 99 research papers. There is a growing trend in research emphasising the security of electronic health records, data storage, access control, and personal health records. The analysis identified the adoption of advanced technologies, including Blockchain and Artificial Intelligence, alongside encryption in developing resilient solutions. These technologies lay the foundations for addressing the prevailing cybersecurity threats, with a particular focus on hacking or malicious attacks, followed by unauthorised access. The research highlights the development of strategies to mitigate data breaches and stresses the importance of technological progress in strengthening data security. The paper outlines future directions, highlighting the need for continuous technological progress and identifying the gaps in the attack mitigations.

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RETRACTED ARTICLE: IoT-based intrusion detection system for healthcare using RNNBiLSTM deep learning strategy with custom features

Cited by 6 publications

References 25 publications

A lightweight intrusion detection method for IoT based on deep learning and dynamic quantization

A lightweight intrusion detection method for IoT based on deep learning and dynamic quantization

Federated Learning for Collaborative Cybersecurity of Distributed Healthcare

Data breaches in healthcare: security mechanisms for attack mitigation

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