Automatic Street Light Controller using LDR and IOT

Abstract

The paper presents an Advanced Keylogger Detection System designed to protect endpoint devices from stealthy malware that records keystrokes and steals sensitive data such as passwords and financial information. It addresses the limitations of traditional signature-based antivirus systems, which fail to detect zero-day and behavior-mimicking keyloggers. The proposed solution uses a hybrid machine learning approach combining: Isolation Forest for unsupervised anomaly detection of abnormal system behavior (CPU usage, keystroke rate, process activity, etc.) LSTM (Long Short-Term Memory) networks for analyzing sequential and time-dependent behavior patterns to detect persistent or low-intensity keylogging activity The system collects real-time behavioral telemetry (keystroke rate, CPU usage, timing intervals, and network activity), preprocesses it, and evaluates it through a two-stage detection pipeline. A hybrid decision engine combines both model outputs to classify activity as normal, medium risk, or high risk. When suspicious behavior is detected, the system triggers real-time alerts, process termination, and forensic logging, all visualized through a dashboard built using FastAPI and WebSockets. Experimental results show that: Normal system behavior is correctly classified with low false positives The hybrid system effectively distinguishes normal, anomalous, and malicious patterns It operates in real time with stable performance and low overhead Overall, the approach improves endpoint security by combining anomaly detection and temporal behavior modeling, enabling more accurate and adaptive detection of modern keyloggers compared to traditional rule-based systems.

Introduction

The paper proposes a real-time Advanced Keylogger Detection System to protect endpoints from stealthy malware that captures keystrokes and sensitive user data. It addresses the weaknesses of traditional signature-based antivirus tools, which struggle with zero-day and behavior-mimicking attacks.

The system uses a hybrid machine learning approach:

• Isolation Forest to detect abnormal system behavior without labeled data
• LSTM to analyze sequential behavior patterns over time and identify persistent keylogging activity

It processes real-time telemetry such as keystroke rate, CPU usage, timing intervals, and network activity, then uses a two-stage pipeline with a hybrid decision engine to classify activity as normal, medium, or high threat.

When threats are detected, the system triggers alerts, logs forensic data, and can terminate suspicious processes, all displayed via a real-time dashboard.

Results show high effectiveness with low false positives, strong detection accuracy, and stable real-time performance. Overall, combining anomaly detection with temporal modeling significantly improves keylogger detection compared to traditional rule-based systems.

Conclusion

The IoT-based street light controller and monitoring system represent a transformative solution for modernizing urban lighting infrastructure, enhancing efficiency, reliability, and sustainability. Through the integration of advanced sensors, controllers, and data analytics, the system offers numerous benefits, including energy savings, operational efficiency, and improved user experience. The IoT-based street light controller and monitoring system of feramultifaceted approach to urban lighting management, delivering a wide range of benefits across environmental, economic, social, and policy dimensions. Ascities embrace digital transformation and strive towards sustainability and resilience goals, the adoption of smart lighting solutions becomes increasingly imperative. The conclusion underscores the transformative potential of the IoT- based street light controller and monitoring system in shaping the future of urban environments and advancing the collective well-being of society. Beyond the technical and economic benefits, the IoT-based street light controller and monitoring system have a significant social impact on communities. Improved street lighting enhances public spaces, fosters a sense of security, and promotes social inclusion and community well-being. It contributes to creating vibrant, livable cities where residents can thrive and businesses can prosper.

References

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Copyright

Copyright © 2026 Divyanshu Singh, Tejanshu Singh. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.