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International Journal for Research Trends and Innovation
International Peer Reviewed & Refereed Journals, Open Access Journal
ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Issue: July 2026
Volume 11 | Issue 7
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Impact Factor : 8.14
Issue per Year : 12
Volume Published : 11
Issue Published : 122
Article Submitted : 25327
Article Published : 9497
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| Paper Title: | A Secure And Privacy Aware Federated Deep Learning System for Home Intrusion Detection |
| Authors Name: | Kavitha C , Aravinda T V , Krishnareddy K R , Ramesh B E |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJRTI_212928
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| Published Paper Id: | IJRTI2605155 |
| Published In: | Volume 11 Issue 5, May-2026 |
| DOI: | https://doi.org/10.56975/ijrti.v11i5.212928 |
| Abstract: | With the rapid growth of smart home technologies and intelligent surveillance systems, ensuring security while protecting user privacy has become increasingly important. Traditional surveillance systems usually rely on centralized servers where video data is continuously stored and processed. However, this method can expose sensitive personal information, increase communication costs, and create risks related to data leakage and unauthorized access. To overcome these challenges, the proposed work introduces a hybrid software-based and hardware-assisted Home Intrusion Detection System that uses Federated Learning for secure and efficient surveillance. In this system, multiple smart home environments are simulated as virtual clients within a single platform. Each client captures video data from cameras and trains its own local intrusion detection model independently. Instead of sending raw video footage to a central server, only model parameters are shared, helping preserve user privacy and reduce network overhead. The framework applies computer vision and anomaly detection techniques to identify suspicious activities such as unauthorized access, unknown persons, and unusual movements. A central server aggregates the locally trained models to create a global model, which is redistributed to clients for continuous improvement. Overall, the system provides a scalable, privacy-preserving, and cost-effective solution for next-generation smart home security. |
| Keywords: | Home Intrusion Detection, Federated Learning, Anomaly Detection, Computer Vision, Multi-Camera Surveillance, Privacy-Preserving Learning, YOLO (You Only Look Once) |
| Cite Article: | "A Secure And Privacy Aware Federated Deep Learning System for Home Intrusion Detection", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.11, Issue 5, page no.b459-b464, May-2026, Available :http://www.ijrti.org/papers/IJRTI2605155.pdf |
| Downloads: | 000169 |
| ISSN: |
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
| Publication Details: |
Published Paper ID: IJRTI2605155
Registration ID:212928
Published In: Volume 11 Issue 5, May-2026
DOI (Digital Object Identifier): https://doi.org/10.56975/ijrti.v11i5.212928
Page No: b459-b464 Country: Chitradurga, Karnataka, India Research Area: Computer Science & Technology Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2605155 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2605155 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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