Enhancing cybersecurity through script development using machine and deep learning for advanced threat mitigation DOI Creative Commons
Tae‐Hoon Kim, Asadi Srinivasulu, Ravi Kumar Cv

и другие.

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Март 10, 2025

This scholarly paper explores the utilization of Machine Learning (ML) and Deep (DL) methodologies to enhance cybersecurity aspects script development. Given increasing panorama threats in contemporary software creation, has ascended a critical realm concern. Traditional security measures frequently prove inadequate countering complex breaches. However, ML DL present promising solutions by facilitating automated intelligent scrutiny security-centric tasks. In this investigation, we leverage Fashion MNIST dataset, deploying Convolutional Neural Network (CNN) model underscore efficacy elevating cybersecurity. The trajectory development encompasses stages like data preprocessing, training, assessment through metrics such as accuracy loss. Our empirical findings convincingly demonstrate that proposed methodology yields significant enhancements benchmarks, thereby validating potential techniques reinforcing security. Furthermore, explore practical implications delineate application ML/DL integration within real scenarios. Through adept amalgamation development, developers can augment robustness their systems against various threats. enriches growing body research while providing invaluable insights practitioners striving bolster resilience ever-evolving landscape challenges.

Язык: Английский

A blockchain-based approach for secure energy-efficient IoT-based Wireless Sensor Networks for smart cities DOI
Hamad Aldawsari

Alexandria Engineering Journal, Год журнала: 2025, Номер 126, С. 1 - 7

Опубликована: Апрель 25, 2025

Язык: Английский

Процитировано

1
Enhancing cybersecurity through script development using machine and deep learning for advanced threat mitigation DOI Creative Commons
Tae‐Hoon Kim, Asadi Srinivasulu, Ravi Kumar Cv

и другие.

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Март 10, 2025

This scholarly paper explores the utilization of Machine Learning (ML) and Deep (DL) methodologies to enhance cybersecurity aspects script development. Given increasing panorama threats in contemporary software creation, has ascended a critical realm concern. Traditional security measures frequently prove inadequate countering complex breaches. However, ML DL present promising solutions by facilitating automated intelligent scrutiny security-centric tasks. In this investigation, we leverage Fashion MNIST dataset, deploying Convolutional Neural Network (CNN) model underscore efficacy elevating cybersecurity. The trajectory development encompasses stages like data preprocessing, training, assessment through metrics such as accuracy loss. Our empirical findings convincingly demonstrate that proposed methodology yields significant enhancements benchmarks, thereby validating potential techniques reinforcing security. Furthermore, explore practical implications delineate application ML/DL integration within real scenarios. Through adept amalgamation development, developers can augment robustness their systems against various threats. enriches growing body research while providing invaluable insights practitioners striving bolster resilience ever-evolving landscape challenges.

Язык: Английский

Процитировано

0