IoT Based Smart Alert Network Security System Using Machine Learning

Anas Habib Zuberi, Shish Ahmad
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Abstract

The increasing security threats in public places such as airports, train stations, and shopping malls require the development of smart security systems that can detect potential threats and provide timely alerts to security personnel. This research paper proposes an IoT-based smart alert network security system using machine learning to enhance public safety. The system integrates various sensors and devices that collect data such as motion, which is analyzed using machine learning algorithms to detect anomalies and trigger alerts if any suspicious activity is detected. The proposed system achieves an accuracy rate of 91.12% in detecting suspicious activities, which is significantly higher than the existing security systems used in public places. The system can provide real-time alerts, which can reduce the response time of security personnel and prevent potential security threats. The proposed system can be implemented in various public places to enhance public safety and prevent security breaches. The results of this research paper provide a useful reference for future studies on the development of smart security systems using IoT and machine learning technologies.
使用机器学习的基于物联网的智能警报网络安全系统
机场、火车站、商场等公共场所的安全威胁日益增加,需要智能安防系统的发展,能够发现潜在的威胁,并及时向安全人员发出警报。本文提出了一种基于物联网的智能报警网络安全系统,利用机器学习来增强公共安全。该系统集成了收集运动等数据的各种传感器和设备,使用机器学习算法对数据进行分析,以检测异常情况,并在检测到任何可疑活动时触发警报。本系统对可疑活动的检测准确率达到91.12%,明显高于现有公共场所安防系统。系统提供实时报警,减少安全人员的响应时间,防范潜在的安全威胁。建议的系统可在多个公共场所实施,以加强公共安全及防止保安漏洞。本文的研究结果为未来使用物联网和机器学习技术开发智能安全系统的研究提供了有益的参考。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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