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Smart city article details
| Title | Securing Iot-Enabled Web Applications And Enhancing Women'S Safety Through Advanced Technologies |
|---|---|
| ID_Doc | 47759 |
| Authors | Singh N.T.; Dhiman R.; Yadav A.L.; Tanwar H.; Kumar M.; Kumar G.; Ruhela A.K. |
| Year | 2024 |
| Published | 2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024 |
| DOI | http://dx.doi.org/10.1109/ISCS61804.2024.10581319 |
| Abstract | The Internet of Things (IoT) is rapidly transforming the way we connect and control physical devices, leading to increased efficiency and financial benefits in various domains, including smart homes and smart cities. However, the proliferation of IoT devices also raises concerns about security. This paper addresses the critical issue of securing online applications against unauthorized intrusions by discussing various approaches such as passwords, encryption, authentication, and integrity checks. Additionally, it suggests advanced techniques to mitigate vulnerabilities and establish robust security measures. Web applications are particularly vulnerable to security threats due to a lack of research on the relationships between attack tactics, critical parameters, and email-based attacks. To address this gap, the paper emphasizes the need for enhanced security measures in web applications. For emergency situations, a companion app is available to empower women and ensure their safety. This user-friendly app offers features such as safe zone visualization, offline mode, and continuous position tracking, accessible through SOS alerts or voice commands. Facial recognition technology plays a vital role in enhancing security across various sectors, including smart homes, self-driving cars, and surveillance applications. The paper compares advanced detection mechanisms like LBPH, SVM, AdaBoost, and Haar Classifiers to assess their accuracy and features in improving security. This research contributes to the broader discussion on security and innovation in the digital and physical realms. © 2024 IEEE. |
| Author Keywords | facial recognition technology; internet of things (IoT); security vulnerabilities; SQL injection; web applications; women's safety |
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