Dive deep into the academic foundations of QBER—Zeron’s risk quantification model built to bridge technical, economic, and legal (TEL) impacts for strategic cybersecurity planning.
Advancing Cyber Risk Intelligence Through Research
Dive into Zeron’s research papers for deep analysis, data-driven findings, and innovative models that redefine how cyber risk is quantified and managed.
Featured Papers
QBER Research Paper
- QBER Research Paper
- Cyber Risk Quantification
QBER Constant
- The QBER Constant
- Cyber Risk Quantification.
Discover how Zeron’s QBER Constant model redefines CVaR with dynamic exploitability, real-time mitigation efficiency, and sector-specific risk profiling—empowering smarter, quantifiable cybersecurity decisions.
PcapNinja Research
- PcapNinja Research
- PCAP Tool, PcapNinja
Explore how PcapNinja uses machine learning and real-time traffic analysis to detect threats and visualize protocol behavior.
Vulnerability Weightage and Prioritization Model: Derived from Real Data and Community Insights
- IEEE COMPSAC 2025
S. Chakraborty, S. Das and S. Bardhan, “Vulnerability Weightage and Prioritization Model: Derived from Real Data and Community Insights,” 2025 IEEE 49th Annual Computers, Software, and Applications Conference (COMPSAC), Toronto, ON, Canada, 2025, pp. 1506-1509, doi: 10.1109/COMPSAC65507.2025.00190.
Traditional scoring systems like CVSS often lack real-time threat adaptability. This work introduces an enhanced vulnerability prioritization model integrating CVSS, EPSS, CWE, CAPEC, MITRE ATT&CK mappings and other community-driven data, leveraging real-time intelligence and a weighted scoring system to prioritize vulnerabilities and strengthen overall security posture.
Extending the Attack Graph Model: Integrating Reconnaissance Stages
- IEEE COMPSAC 2025
S. Chakraborty, O. Shaikh and S. Bardhan, “Extending the Attack Graph Model: Integrating Reconnaissance Stages,” 2025 IEEE 49th Annual Computers, Software, and Applications Conference (COMPSAC), Toronto, ON, Canada, 2025, pp. 1518-1521, doi: 10.1109/COMPSAC65507.2025.00193.
Reconnaissance plays a pivotal role in most cyber attacks yet is absent from the widely-adopted NIST attack-graph model. This paper integrates reconnaissance into attack graphs so defenders can visualise all attack paths — including reconnaissance stages — bridging the gap between reconnaissance and active attack stages, with a detailed case study.
Vendor PulseGen: Generative Vendor Risk Management Platform
- IEEE COMPSAC 2025
S. Yasasvi, N. Sinhababu, S. K. Chakraborty and S. Bardhan, “Vendor PulseGen: Generative Vendor Risk Management Platform,” 2025 IEEE 49th Annual Computers, Software, and Applications Conference (COMPSAC), Toronto, ON, Canada, 2025, pp. 1498-1501, doi: 10.1109/COMPSAC65507.2025.00188.
Vendor PulseGen uses the non-parametric, in-context learning abilities of LLMs to generate tailored vendor-risk questionnaires from publicly available vendor metadata, then assigns quantitative scores across risk dimensions — Question Risk Score (QRS), Compliance Risk Score (CRS) and External Surface Risk (ESR) — for a quantified view of third-party risk.
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