Mirai Botnet Attacks on IoT Applications: Challenges and Controls

Sripriyanka, G.; Mahendran, Anand

doi:10.1007/978-3-031-13150-9_5

Cited by 2 publications

(2 citation statements)

References 39 publications

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“…This hybrid optimization model is computationally efficient, can able to learn quickly and making it suitable for large scale real-time applications called smart healthcare applications. This makes it an ideal choice for a range of applications, from predictive modeling to data classification and the equations (6,7,16) for selection, crossover, mutation, and fitness functions in data preprocessing on CXR images. And this hybrid optimization model is cost-effective, making it viable option for large-scale smart healthcare applications, and it will produce consistent results.…”

Section: Hgwo-pso Based Preprocessingmentioning

confidence: 99%

“…Using the private key, the decryption procedure transforms the data that was encrypted using the cipher into the original image. The decryption process is essentially the same as the encryption process; however, it applies the steps in the opposite order [7]. When it comes to encryption, the function of the secret keys is of the utmost importance.…”

Section: Introductionmentioning

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: Hgwo-pso Based Preprocessingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%