Asset Details
Do you wish to reserve the book?
A computational framework for IoT security integrating deep learning-based semantic algorithms for real-time threat response
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
A computational framework for IoT security integrating deep learning-based semantic algorithms for real-time threat response
Do you wish to request the book?
A computational framework for IoT security integrating deep learning-based semantic algorithms for real-time threat response
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Journal Article
A computational framework for IoT security integrating deep learning-based semantic algorithms for real-time threat response
2025
Overview
The growth of IoT networks has led to significant security issues, especially in areas of real-time threat detection and response. This research paper presents a hybrid deep learning and semantic reasoning framework that enhances threat intelligence and autonomous response. The proposed research framework integrates Convolutional Neural Networks for spatial anomaly detection and Recurrent Neural Networks for sequential pattern recognition. Concurrently, a semantic contextualization layer utilizes knowledge graphs for context-aware threat detection. The model is highly computational and energy efficient, incorporating path-breaking Edge Computing and Real-Time Stream Processing paradigms, facilitating low-latency identification of highly dynamic advanced attacks like APTs and DDoS. During this research study, extensive statistical validation was performed using the CICIoT 2023 dataset and a custom Internet of Things testbed, demonstrating high accuracy, scalability, and adaptability across diverse IoT environments. The paper also outlines privacy, ethical considerations, and regulatory compliance (GDPR, CCPA) to ensure responsible deployment. This research contributes to next-generation autonomous IoT security solutions, bridging deep learning, semantic reasoning, and real-world security challenges, with future work focusing on real-world deployments and adaptive threat intelligence.
Publisher
Nature Publishing Group UK,Nature Publishing Group,Nature Portfolio
Subject
We currently cannot retrieve any items related to this title. Kindly check back at a later time.