DDoS Attacks Impact on Data Transfer in IOT-MANET-Based E-Healthcare for Tackling COVID-19

Ashu,; Mahajan, Rashima; Zafar, Sherin

doi:10.1007/978-981-15-8335-3_24

Cited by 9 publications

(5 citation statements)

References 16 publications

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“…They detected man-in-the-middle attacks but did not address DDoS attacks. In [39], the authors developed the simulated sheltered ad hoc on-demand vector (SAODV) transmission protocol for data transfer in the IoMT environment, specifically considering COVID-19-related data transfer. In [40], the authors studied the analysis of DDoS attacks that occurred during the COVID-19 epidemic.…”

Section: Survey On Ddos-based Security Protocols In Covid-19 Environmentmentioning

confidence: 99%

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: Survey On Ddos-based Security Protocols In Covid-19 Environmentmentioning

confidence: 99%

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

Sripriyanka,

Mahendran

2024

IEEE Access

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“…Online medical check-up Sensors 2023, 23, 6850 6 of 25 applications in this era can be immensely helpful for patients with critical conditions, as they can monitor themselves from a mobile location [37]. Patient records containing essential information, such as name, age, gender, sugar level, and blood information, must be accurately monitored [38]. Real-time anomaly detection plays a vital role in tracking patients' services and in detecting anomalies promptly.…”

Section: Architecture Of High-speed Networkmentioning

confidence: 99%

High-Speed Network DDoS Attack Detection: A Survey

Haseeb-ur-rehman,

Aman,

Hasan

et al. 2023

Sensors

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Having a large number of device connections provides attackers with multiple ways to attack a network. This situation can lead to distributed denial-of-service (DDoS) attacks, which can cause fiscal harm and corrupt data. Thus, irregularity detection in traffic data is crucial in detecting malicious behavior in a network, which is essential for network security and the integrity of modern Cyber–Physical Systems (CPS). Nevertheless, studies have shown that current techniques are ineffective at detecting DDoS attacks on networks, especially in the case of high-speed networks (HSN), as detecting attacks on the latter is very complex due to their fast packet processing. This review aims to study and compare different approaches to detecting DDoS attacks, using machine learning (ML) techniques such as k-means, K-Nearest Neighbors (KNN), and Naive Bayes (NB) used in intrusion detection systems (IDSs) and flow-based IDSs, and expresses data paths for packet filtering for HSN performance. This review highlights the high-speed network accuracy evaluation factors, provides a detailed DDoS attack taxonomy, and classifies detection techniques. Moreover, the existing literature is inspected through a qualitative analysis, with respect to the factors extracted from the presented taxonomy of irregular traffic pattern detection. Different research directions are suggested to support researchers in identifying and designing the optimal solution by highlighting the issues and challenges of DDoS attacks on high-speed networks.

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“…Ransomware typically attacks victim machines in several ways including phishing emails, malicious links, and malvertisings [20] [21]. Networkbased attacks such as Denial-of-Service (DoS), Distributed Denial of service (DDoS), and buffer-overflow attacks are on the rise [22] [23].…”

Section: Analysis Of Security Issues In Future Healthcare Systemsmentioning

confidence: 99%