Year: 2025
Type: article
Abstract: Combining machine learning with the Django framework significantly enhances intrusion detection in Internet of Things (IoT) environments. This system incorporates powerful classification models Random Forest, Bagging, and Ridge to improve detection precision and resilience against cyberattacks. Random Forest utilizes multiple decision trees to accurately identify diverse and complex attack patterns across large datasets. Bagging enhances the model’s robustness by lowering variance through model aggregation, ensuring reliable performance in different intrusion scenarios. Ridge Classifier adds regularization to minimize overfitting, which is especially valuable when handling high-dimensional network data. Django serves as the backbone of the application, offering a user-friendly and scalable interface for real-time intrusion monitoring and response. The synergy between Django and these machine learning models creates a responsive, efficient solution for dynamic IoT security needs. This architecture provides a well-rounded defense mechanism capable of adapting to evolving threats, ensuring comprehensive protection for interconnected IoT systems.
Author: Dr. R. Bharathi
Institution: SRM Institute of Science and Technology
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4410122895