International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | AI Based Intrusion Detection Device |
|---|---|
| ABSTRACT | Security plays a crucial role in residential, institutional, and commercial environments, particularly with the rising occurrences of unauthorized entry and theft. Traditional security mechanisms such as locks, CCTV surveillance, and manual monitoring lack automated real-time intelligence and cannot identify individuals. This paper proposes an AI-based intrusion detection device built using Raspberry Pi, Pi Camera, OpenCV, and IoT communication. The system employs the Haar Cascade classifier for face detection and the Local Binary Pattern Histogram (LBPH) algorithm for face recognition. If a person is identified as an authorized user, the door unlocks automatically through a relay-controlled solenoid lock. If the person is unknown, the system captures an image and immediately sends it to the owner via a Telegram bot. Experimental evaluation demonstrates efficient detection accuracy, fast response time, and reliable performance under normal indoor lighting. The system provides a low-cost, autonomous, and scalable approach to intelligent intrusion detection and real-time access control. |
| AUTHOR | SOURABH D, DR. MALATESH S H, SHINDE RADHA, SHWETA, YASHWANT GOWDA Dept. of Computer Science and Engineering, MS Engineering College, Bengaluru, Karnataka, India Professor& HOD, Dept. of Computer Science and Engineering, MS Engineering College, Bengaluru, Karnataka, India |
| VOLUME | 180 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1401054 |
| pdf/54_AI Based Intrusion Detection Device.pdf | |
| KEYWORDS | |
| References | 1. Viola P., Jones M., “Rapid Object Detection using a Boosted Cascade of Simple Features,” IEEE CVPR. 2. OpenCV, “Face Detection and Recognition Documentation,” 2025. 3. Raspberry Pi Foundation, “Raspberry Pi 4 Model B Technical Specifications,” 2024. 4. Telegram API, “Bot Communication and Image Handling Documentation,” 2025. 5. Pecolt S. et al., “Biometric Recognition Systems for Embedded Platforms,” MDPI, 2025. 6. Guerbaoui M., “Advanced Facial Recognition on Edge Devices,” E3S Conferences, 2025. 7. Aboluhom, A. A., et al., “Face Recognition using Transfer Learning Techniques on Embedded Raspberry Pi Systems,” Oxford University Press (OUP), 2024. 8. S. Maryam & R. Al-Hyari, “Real-Time Intrusion Detection using Edge AI and IoT-based Alert Systems,” IEEE Internet of Things Journal, 2023. 9. Kadambi S., “Smart Surveillance Systems: AI-driven Face Recognition for Secure Access Control,” Elsevier Journal of Smart Security, 2024. |
