ATL – AI THREAT LENS: AN INTELLIGENT REAL-TIME CYBER THREAT DETECTION PLATFORM USING MACHINE LEARNING AND MULTI-ENGINE THREAT INTELLIGENCE

ATL – AI Threat Lens represents a comprehensive and principled approach to accessible cybersecurity threat detection that successfully bridges the gap be- tween advanced security tools and everyday users. The system addresses the fundamental limitations of existing security solutions through a unified, modu- lar, three-tier architecture that combines an ensemble machine learning model trained on 48,729 URLs with fifty engineered features, five integrated real-time threat intelligence APIs, and a weighted composite danger scoring algorithm that synthesizes all availa- ble signals into a single normalized metric. The implementation demonstrates that a full-stack cybersecurity platform built with modern web tech- nologies — React, TypeScript, Express.js, Tail- windCSS, Python, Flask, and scikit-learn — can de- liver enterprise-grade threat analysis capabilities through a consumer-friendly interface. The Server- Sent Events streaming architecture enables an un- precedented level of analysis transparency by expos- ing the complete execution pipeline to the user in real time, transforming threat scanning from a black-box process into an observable, educational experience. The master threat classifier's dual-mode operation for URL and file threats and its twenty-category classifi- cation taxonomy provide actionable, contextually ap- propriate verdicts that support informed user deci- sion-making. The system's memory-efficient file processing archi- tecture, which supports uploads of up to 15 gigabytes through chunked disk streaming and SHA256-based VirusTotal cache lookup, demonstrates that practical constraints of large-scale file scanning can be ad- dressed within the constraints of a web-based archi- tecture. The modular design of all three tiers ensures that the ML model, API integrations, and frontend in- terface can each be independently upgraded as the threat landscape evolves, providing a sustainable platform for long-term cybersecurity capability de- velopment. The ATL system confirms that the com- bination of machine learning, multi-source threat in- telligence, and thoughtful user experience design can significantly improve the accessibility and effective- ness of cybersecurity tools for users across all levels of technical expertise.

[1]   Sahingoz, O. K., Buber, E., Demir, O., Diri, B.: Machine Learning Based Phishing Detection from URLs. Expert Systems with Applications, Vol. 117, pp. 345–357, 2019. [2]  Mohammad, R. M., Thabtah, F., McCluskey, L.: Intelligent Rule-based Phishing Websites Classifica- tion. IET Information Security, Vol. 8, No. 3, pp. 153–160, 2014. [3]  Gutierrez, C. N., Kim, T., Della Corte, R., Avery, J., Goldwasser, D., Garg, S., Bagchi, S.: Learning from the Ones That Got Away: Detecting New Forms of Phishing Attacks. IEEE Transactions on Dependa- ble and Secure Computing, 2018. [4]  Buber, E., Diri, B., Sahingoz, O. K.: NLP Based Phishing Attack Detection from URLs. International Conference on Intelligent Systems Design and Appli- cations (ISDA), pp. 608–618, 2017. [5]  Rajab, M. A., Ballard, L., Mavrommatis, P., Pro- vos, N., Zhao, X.: The Nocebo Effect on the Web: An Analysis of Fake Anti-Virus Distribution. USENIX Workshop on Large-Scale Exploits and Emergent Threats (LEET), 2010. [6]  Chen, T., Guestrin, C.: XGBoost: A Scalable Tree Boosting System. ACM SIGKDD International Con- ference on Knowledge Discovery and Data Mining, pp. 785–794, 2016. [7]  Breiman, L.: Random Forests. Machine Learning, Vol. 45, No. 1, pp. 5–32, 2001. [8]     VirusTotal. VirusTotal API v3 Documenta- tion.Availableat:       https://developers.virusto- tal.com/reference/overview (accessed April 2026). [9]    Google. Safe Browsing API Documentation v4.Availableat: https://developers.google.com/safe- browsing (accessed April 2026). [10]  IPQualityScore. URL Reputation and Malicious URL Lookup API. Available at: https://www.ipqual- ityscore.com/documentation (accessed April 2026).