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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM65344
Volume:
10 |
6 |
Jun 2026
Pages:
1-9
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

Department of Electronics & Telecommunication
Sinhgad College of Engineering, Vadgoan bk, Pune
Department of Electronics & Telecommunication
Sinhgad College of Engineering, Vadgoan bk, Pune
Department of Electronics & Telecommunication
Sinhgad College of Engineering, Vadgoan bk, Pune

Department of Electronics & Telecommunication
Sinhgad College of Engineering, Vadgoan bk, Pune
Increasing incidents of ATM tampering and unauthorized access require smart automated defenses. This project presents an AI Based Secure ATM Access and Intrusion Response System built around a Raspberry Pi 4B. The system integrates an IR sensor and vibration sensor for early detection, a USB camera for AI-powered visual analysis, a SIM800C GSM module for immediate alerts, a solenoid lock for automatic enclosure control, and a relay-driven current-transferring coil as an active deterrent. On detecting suspicious activity the system locks the enclosure, energizes the coil, sounds an alarm, captures evidence and sends SMS alerts to predefined numbers. The architecture aims to provide real-time, low-latency autonomous protection suitable for both standalone ATMs and integration into existing bank security infrastructure.
KEYWORDS
The implementation results confirm that the system operates efficiently in real-time environments with low latency and high reliability. The automated response mechanisms, including solenoid lock activation and alarm triggering, provide immediate physical security control, making the system proactive rather than reactive. Overall, the proposed solution offers a cost-effective, scalable, and intelligent framework for modern ATM security. It significantly improves threat detection accuracy, reduces dependency on human surveillance, and enhances overall system responsiveness. This makes it suitable for deployment in banking environments where security, speed, and reliability are critical requirements.
[1]. Degadwala, Sheshang, et al. “Advancements in ATM Security for Movement and Tampering Detection: A Review.” International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2024. Available at: https://doi.org/10.32628/CSEIT2410587. [2]. Sanserwal, Vishal, et al. “Comparative Analysis of Various Feature Descriptors for Efficient ATM Surveillance Framework.” Proceeding - IEEE International Conference on Computing, [3]. Communication and Automation, ICCCA 2017, 2017, pp. 539–44.Availableat: https://doi.org/10.1109/CCAA.2017.8229860. [4]. Liu, Fan, et al. “Abnormal Behavior Recognition System for ATM Monitoring by RGB-D Camera.” MM 2012 - Proceedings of the 20th ACM International Conference on Multimedia, 2012, pp. 1295 – 96. Available at: https://doi.org/10.1145/2393347.2396450. [5]. Wu J-D, Ye S-H (2009) Driver identification using finger-vein patterns with Radon transform and neural network. Expert SystAppl 36(3, Part 2):5793–5799. [6]. Researcher Wu and Liu have described neural network and component analysis methodology used in finger vein authentication method.
Publication Date
6/23/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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