Location
https://www.kennesaw.edu/ccse/events/computing-showcase/fa24-cday-program.php
Event Website
https://cybriant-attack-surface-management-t1.github.io
Document Type
Event
Start Date
19-11-2024 4:00 PM
Description
Recent advancements in AI have made knowledge more accessible, but this also introduces risks, as vulnerabilities can now be quickly found and exploited. To address this, we developed a comprehensive, cloud-native attack surface monitoring suite in Google Cloud. Integrating open-source intelligence tools like OWASP Amass and Project Discovery, along with custom Python-based processing, we gather extensive security data—covering subdomain enumeration, open ports, HTTP responses, and DNS configurations. This data is stored in BigQuery, processed, and visualized in Looker Studio for easy client interpretation. A containerized, scalable backend with a Flask-based API ensures seamless tool integration and adaptability. BigQuery ML further classifies domains’ security, empowering organizations with proactive risk assessment and attack surface monitoring.
Included in
UC-144 Attack Surface Management and Analysis
https://www.kennesaw.edu/ccse/events/computing-showcase/fa24-cday-program.php
Recent advancements in AI have made knowledge more accessible, but this also introduces risks, as vulnerabilities can now be quickly found and exploited. To address this, we developed a comprehensive, cloud-native attack surface monitoring suite in Google Cloud. Integrating open-source intelligence tools like OWASP Amass and Project Discovery, along with custom Python-based processing, we gather extensive security data—covering subdomain enumeration, open ports, HTTP responses, and DNS configurations. This data is stored in BigQuery, processed, and visualized in Looker Studio for easy client interpretation. A containerized, scalable backend with a Flask-based API ensures seamless tool integration and adaptability. BigQuery ML further classifies domains’ security, empowering organizations with proactive risk assessment and attack surface monitoring.
https://digitalcommons.kennesaw.edu/cday/Fall_2024/Undergraduate_Project/7