Web guard: Secure Web page access control and URL filtering system
- 1Anna University Chennai, Assistant Professor, Department of Information technology, VelTech HiTech, Chennai, IN
The increasing reliance on digital platforms in office environments has led to a rise in cyber threats and online criminal activities. To address these challenges, this project proposes a comprehensive system for crime detection and malicious URL management within office webpages. The system leverages the power of ensemble machine learning algorithms, including Decision Tree Classifier, Random Forest Classifier, AdaBoost Classifier, KN Neighbors Classifier, SGD Classifier, Extra Tree Classifier, and Gaussian Naive Bayes, to enhance the accuracy and efficiency of the detection process.
Conclusion
Digital Forensics Detection of ongoing Cyber Attacks presents a comprehensive solution for cybercrime detection in office webpages, focusing on malicious URL identification. Leveraging a diverse set of machine learning algorithms and ensemble learning techniques, the system achieves robust threat analysis. The real-time monitoring and alerting system ensure swift response to potential security threats, reducing false positives and negatives. The user-friendly interface empowers administrators for effective system management. The project's adaptability to dynamic threats and seamless integration with existing security measures provide a resilient and scalable cybersecurity solution. Successful implementation promises enhanced security for digital workspaces, safeguarding sensitive data and ensuring business continuity. The project's outcomes contribute to the evolving field of cybercrime detection and fortify organizations against the ever-changing landscape of cyber threats.
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Keywords: Url detection, Employee Management
Citation: C.K. Shruthi*, C.K. Shruthi ( 2024), Web guard: Secure Web page access control and URL filtering system. , 12(2): 1-6
Received: 09/06/2024; Accepted: 09/06/2024;
Published: 09/06/2024
Edited by:
Mr.ERES JOURNALS