LEAESN: Predicting DDoS attack in healthcare systems based on Lyapunov Exponent Analysis and Echo State Neural Networks

Salemi, Hossein; Rostami, Habib; Azad, Saeed Talatian; Khosravi, Mohammad R.

doi:10.1007/s11042-020-10179-y

Cited by 10 publications

(6 citation statements)

References 31 publications

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“…Table 3 and Figure 9 compare the statistical performance comparison of various DDoS attack detection methods simulated on the KSD dataset. Here, the proposed ICDC-Net resulted in improved performance over existing MOA-WMA [20], DT-IDS [22], XSRU [25], and LEAESN [24] methods for all metrics. The proposed method improved specificity by 1.13%, accuracy by 1.02%, sensitivity by 1.23%, False Acceptance Rate (FAR) by 1.99%, False Rejection Rate (FRR) by 1.01%, False Non-Match Rate (FNMR) by 1.45%, and False Match Rate (FMR) by 1.28%.…”

Section: B Ddos Attack Detection Performancementioning

confidence: 84%

“…MOA-WMA [20] DT-IDS [22] XSRU [25] LEAESN [24] Proposed Sensitivity (%) Here, the proposed ICDC-Net improved the sensitivity by 1.23%, accuracy by 1.75%, FRR by 1.89%, specificity by 2.01%, precision by 2.11%, recall by 2.13%, FMR by 1.12%, FAR by 1.83%, and FNMR by 1.37% performance metrics. Here, the proposed method performance is improved as compared to existing EDLM [26], WOT-UNET [28], DFM [32], and C-GAN [36] methods.…”

Section: Methodsmentioning

confidence: 91%

“…Mitigating DDoS attacks is challenging due to the complexities involved in filtering out malicious traffic without disrupting legitimate traffic. Overall, the significance of DDoS attacks lies in their potential to disrupt online services, their wide accessibility, and the challenges they pose in terms of prevention and mitigation [24].…”

Section: A Security Vulnerabilities In the Iomtmentioning

confidence: 99%

“…In [23], the authors developed man-in-the-middle (MitM) attack detection using PSO optimization, which resulted in increased false alarm rates and attack detection time. In [24], the authors developed the Lyapunov Exponent Analysis and Echo State Network (LEAESN) using a recurrent neural echo state network (SCESN) model to predict DDoS attacks in an IoMT environment using the Darpa98 dataset. Additionally, the authors of [25] developed the explainable recurrent units (XSRU) for predicting attacks in the IoMT environment.…”

Section: A Survey On Ddos Attack Security Protocolsmentioning

confidence: 99%

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Securing IoMT: A Hybrid Model for DDoS Attack Detection and COVID-19 Classification

Sripriyanka,

Mahendran

2024

IEEE Access

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The coronavirus disease 2019 (COVID-19) epidemic had a momentous influence on the state of universal health and how people live their lives in every nation. The combination of the Internet of Things (IoT) with medical systems is referred to as the "Internet of Medical Things (IoMT)," and it makes it possible for various medical events to take place, including real-time medicine prescriptions, remote patient observing, and real-time diagnosis, amongst other things. However, the security, integrity, and concealment of medical data on the IoMT remain an endless issue, contributing to the difficulties that arise in providing medical services. However, these conventional methods failed to provide robust security against attacks like distributed denial of service (DDoS). As a result, security IoMT-based COVID-19 detection applications are required. Therefore, this article proposes a novel IoMT-based COVID-19 detection and classification network (ICDC-Net) for smart healthcare applications. Initially, Optimal Feistel Block Cipher (OFBC) encryption was performed on COVID-19-based medical data (chest x-ray images), which provides the highest security to the patient's data. Here, OFBC loss is optimized by the Hybrid Grey-Wolf Optimizer with Particle Swarm Optimization (HGWO-PSO), which performs DDoS attack detection and prevention. Further, the HGWO-PSO is also used to extract disease-specific features from the COVID-19 chest X-ray (CXR) images. Finally, the Residual Network50 (ResNet50) deep learning model is used to classify multiple diseases from CXR images, including normal, COVID-19, and viral pneumonia. The simulations disclosed that the proposed ICDC-Net resulted in improved attack detection accuracy (ADA), attack detection time (ADT), and reduced attack detection error rates (ADER) as compared to other security standards. Further, the simulations also disclosed that the proposed ICDC-Net developed superior COVID-19 categorization performing as equated to existing classification prototypes.

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Section: B Ddos Attack Detection Performancementioning

confidence: 84%

Section: Methodsmentioning

confidence: 91%

Section: A Security Vulnerabilities In the Iomtmentioning

confidence: 99%

Section: A Survey On Ddos Attack Security Protocolsmentioning

confidence: 99%

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Securing IoMT: A Hybrid Model for DDoS Attack Detection and COVID-19 Classification

Sripriyanka,

Mahendran

2024

IEEE Access

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“…In a recent work performed by (Salemi et al, 2021) in [89], the authors predict rather than detect DDoS attacks in the healthcare system. For this purpose, the authors proved that a DDoS attack in the traffic makes the time series data chaotic, which allows applying the method of Lyapunov Expansions Analysis and the Echo State Network to predict DDoS attacks.…”

Section: For Transmission Level Securitymentioning

confidence: 99%

Survey of Machine Learning Based Intrusion Detection Methods for Internet of Medical Things

Si-Ahmed¹,

Al-Garadi²,

Boustia³

2022

Preprint

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Internet of Medical Things (IoMT) represents an application of the Internet of Things, where health professionals perform remote analysis of physiological data collected using sensors that are associated with patients, allowing real-time and permanent monitoring of the patient's health condition and the detection of possible diseases at an early stage. However, the use of wireless communication for data transfer exposes this data to cyberattacks, and the sensitive and private nature of this data may represent a prime interest for attackers. The use of traditional security methods on equipment that is limited in terms of storage and computing capacity is ineffective. In this context, we have performed a comprehensive survey to investigate the use of the intrusion detection system based on machine learning (ML) for IoMT security. We presented the generic three-layer architecture of IoMT, the security requirement of IoMT security. We review the various threats that can affect IoMT security and identify the advantage, disadvantages, methods, and datasets used in each solution based on ML. Then we provide some challenges and limitations of applying ML on each layer of IoMT, which can serve as direction for future study.

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Distributed and Big Health Data Processing for Remote and Ubiquitous Healthcare Services Using Blind Statistical Computing: Review and Trends on Blindness for Internet of Artificially Intelligent Medical Things

Khosravi

2022

Intelligent Healthcare

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LEAESN: Predicting DDoS attack in healthcare systems based on Lyapunov Exponent Analysis and Echo State Neural Networks

Cited by 10 publications

References 31 publications

Securing IoMT: A Hybrid Model for DDoS Attack Detection and COVID-19 Classification

Securing IoMT: A Hybrid Model for DDoS Attack Detection and COVID-19 Classification

Survey of Machine Learning Based Intrusion Detection Methods for Internet of Medical Things

Distributed and Big Health Data Processing for Remote and Ubiquitous Healthcare Services Using Blind Statistical Computing: Review and Trends on Blindness for Internet of Artificially Intelligent Medical Things

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