CH 7 BMGT 8434 Topic Definition Statement

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Capella University *

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8432

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Management

Date

Jan 9, 2024

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docx

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4

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RUNNING HEAD TITLE DISTRIBUTED DENIAL-OF-SERVICES AND IOT Distributed denial-of-services and IoT Capella University Course Number: BMGT8432
RUNNING HEAD TITLE DISTRIBUTED DENIAL-OF-SERVICES AND IOT 2 Research problem. In cloud computing, DDoS attacks are disastrous due to socioeconomic advancements in technological growth phenomenal. IoT and cloud computing methods have been used for identifying low-rate denial of service (DDoS) assaults using the computational framework and comparable metrics for temporal mining. Reliability and rate of identification are enhanced by using a computational learning model with computationally efficient feature reduction. There still needs to be more in our comprehension of traffic non-linear behavior, as evidenced by research on network traffic assaults in cloud services, the Internet of Things, and other contemporary networks. Regression methods need to be more utilized, and additional network traffic distribution consideration is required for machine learning-based detection in cloud environments. For effective intrusion detection and prevention systems, these datasets are essential. Recent studies have examined DoS assaults, analyzed IDS datasets, and incorporated AI and machine learning architectures (Sambangi et al., 2022). Flash incidents and ongoing disruptions have shown how important it is for researchers to investigate assaults, security issues, and IoT layers for increased safety. Blockchain layers detect DDoS attacks and defend against them. The universal architecture, standardized framework for IoT, and challenges to safeguarding IoT networks from DDoS attacks still need to be fixed (Kumari & Jain, 2023). Research problem background.
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