behavioral analysis and machine learning models could contribute to better accuracy.
● Enhancing User Experience: Future versions could include more advanced user interface features, such as customizable notifications, interactive feedback, and integration with password managers for enhanced security.
● Expanding Threat Detection: Additional threat intelligence feeds and machine learning-based anomaly detection could improve detection capabilities, particularly for emerging threats that are not yet widely recognized.
Some Picture of Secure Network monitor
Figure 6. UI of the extension
Figure 7. Download logs
Conclusion
The Secure Network Monitor Chrome extension was developed with the aim of enhancing user security while browsing the web. This extension integrates real-time detection and blocking of suspicious domains and trackers, leveraging threat intelligence feeds and regex-based detection. By doing so, it helps protect users from phishing attacks, trackers, and malicious websites, which are significant threats in the modern internet landscape.
Throughout the development process, we implemented several key features, including domain detection, real-time blocking, tracker detection, and a user-friendly interface with the ability to whitelist and manage domains. Performance metrics indicate that the extension operates with minimal resource consumption and provides an effective defense against web-based threats without significantly impacting browsing experience.
The project aligns with current needs for stronger browser-based security tools, and the extension's design allows for scalability and further integration with additional threat intelligence sources and detection algorithms.
Key Achievements
The development of the Secure Network Monitor represents a significant step toward improving web security for users. The extension was able to:
● Accurately detect and block suspicious domains using threat feeds like OpenPhish, PhishTank, and custom rules for trackers.
● Minimize the impact on browser performance, demonstrating its ability to handle real-time traffic analysis with low resource consumption.
● Provide a user-friendly interface, allowing users to manage settings and receive notifications for security events.
● Establish a scalable architecture, allowing for future updates such as machine learning-based anomaly detection and cloud-based log management.
Challenges
The development process was not without challenges. Key obstacles included:
● Data integration and management: Merging data from various threat feeds, ensuring the information was up-to-date and de-duplicated, posed significant technical challenges.
● Real-time performance: Ensuring that the blocking mechanism did not cause latency or interruptions during browsing was an ongoing concern.
● User interface design: Balancing functionality with usability, ensuring the extension was both effective and intuitive for non-technical users.
Despite these challenges, the development of the extension has successfully met the initial project goals, demonstrating its potential for broader use and integration into mainstream web security solutions.
Future Work
While the current implementation of the Secure Network Monitor extension has proven effective, there are several avenues for future development:
● Expanding detection capabilities: Integrating additional threat intelligence sources, as well as incorporating machine learning algorithms for detecting anomalies and zero-day threats, would significantly enhance the extension's security features.
● Improved user experience: Adding more interactive features such as a visual dashboard with detailed analytics (e.g., network activity over time, risk assessments) would provide users with more insights into their browsing behavior
● Performance optimization: Continual efforts to reduce the performance overhead and increase the speed of the detection algorithms will be crucial, especially as more complex security features are integrated.
The future developments and enhancements will ensure that the extension remains a robust and adaptive solution for securing user browsing activities, providing comprehensive protection against ever-evolving online threats.
References
[1] APWG, "Phishing Activity Trends Report," 2023.
[2] Englehardt, S., & Narayanan, A., "Online Tracking: A 1-Million-Site Measurement and Analysis," Proceedings of the ACM Conference on Computer and
Communications Security (CCS)., 2016.
[3] Mozilla Foundation, "State of Mozilla Internet Health Report," 2021.
[4] Cybersecurity & Infrastructure Security Agency (CISA), "Protecting Against Cyber Threats," 2023.
[5] Libert, "Exposing the invisible web: An analysis of third-party HTTP requests on one million websites," International Journal of Communication, 9, p. 3544–3561, 2015.
[6] Symantec, "Annual Cyber Threat Report.," 2022.
[7] Gupta et al, "Challenges in Web-Based Security: A Review," Journal of Cybersecurity Studies,4(2), pp. 55-72, 2021.
[8] Merzdovnik et al, " Block Me If You Can: A Large-Scale Study of Tracker- Blocking Tools," IEEE Security & Privacy 15(4), pp. 55-64, 2017.
[9] e. a. Ikram, "The Usability Challenge of Browser Security Tools: A Survey," IEEE Transactions on Information Forensics & Security, 14(3), pp. 1022-1037., 2019.
[10] Felt and A. P, "Why Users Ignore Security Warnings. USENIX Security Symposium.," 2015.
[11] Google Developer Docs, "DeclarativeNetRequest API Overview," 2023.
Appendix: Project code
Figure 9. Suspicious Domain Detection algorithms
Figure 10. Real-Time Blocking of Suspicious Domains algorithms