Making accurate predictions before acting is crucial for good decision-making. Being able to intelligently guess what might happen next and getting ready for it can really improve our chances of doing well. This idea is also important in managing security risks.
Cybersecurity is a high-stakes race and the winner is the one who finds the weakness first. Most security professionals work against the clock to find and fix system holes before opportunistic malicious actors exploit them.
Traditional Approaches are dated.
Traditional vulnerability management approaches, such as the Common Vulnerability Scoring System (CVSS), are increasingly facing scalability challenges in today’s rapidly evolving cybersecurity landscape. CVSS has been the community-adopted indicator for assessing and communicating the severity of vulnerabilities in computer systems since its introduction in 2005.
The core issue with CVSS lies in its static nature; it assigns a severity score to vulnerabilities based on a fixed set of criteria without considering the dynamic context of each organization’s unique network environment. These traditional approaches don’t account for the ever-changing tactics of threat actors, making it challenging to prioritize vulnerabilities based on real-world threat intelligence. This leads to inefficient allocation of resources, where critical vulnerabilities might be overlooked in the sea of less relevant ones.
According to research by FIRST (Forum of Incident Response and Security Teams), businesses and technology vendors fixonly 5-20% of vulnerabilities every month. Yet, only 2-7% of vulnerabilities are ever exploited. But which ones exactly? Since we cannot be sure which vulnerabilities need to be managed first, and since we cannot fix them all immediately, we need to prioritize.
The World of EPSS
Fast forward to 2019. Exploit Prediction Scoring System (EPSS) was started as an open community-driven effort to model and manage vulnerability risk from a probabilistic perspective. EPSS is considered in research communities and a growing number of enterprises as a modern approach to vulnerability management that addresses the shortcomings of traditional approaches.
EPSS scores range from 0% (the lowest probability of exploitation) to 100% (the highest probability of exploitation). In addition, since it can be hard to extrapolate the true meaning from a probability score alone, EPSS also provides percentile rankings; percentile rankings measure EPSS probability relative to all other EPSS scores. The combination of probability and percentile enables advanced prioritization inputs.
EPSS leverages machine learning algorithms to predict the likelihood of a vulnerability being exploited in the wild in the next 30 days. By integrating real-time data from various sources, including threat intelligence feeds and active exploitation trends, EPSS provides a more dynamic and context-aware assessment. This approach enables organizations to prioritize their remediation efforts more effectively, focusing on vulnerabilities that are not just theoretically severe but are also likely to be targeted by attackers. By doing so, EPSS helps organizations optimize their “scarce” and usually “expensive” security resources, ensuring that they address the most pressing threats, thus enabling a more proactive and efficient vulnerability management process.
Key Aspects of EPSS
- Data Integration: Gathers data from vulnerability databases, threat intelligence, and exploit occurrences.
- Machine Learning: Analyzes data using algorithms to identify exploitation patterns.
- Dynamic Updates: Continuously refreshes data for current threat landscape relevance.
- Risk Evaluation: Considers factors like exploitability, exploit code availability, and software popularity.
- Scoring: Assigns probability scores to vulnerabilities, indicating exploitation likelihood.
- Contextual Tailoring: Adapts scores based on specific network environments and exposure levels.
- Prioritization Aid: Guides efficient response with emphasis on high-probability vulnerabilities.
- Exploitation activity is evidence that exploitation of a vulnerability was attempted, not that it was successful against a vulnerable target. The model collects data from honeypots, IDS/IPS sensors, and host-based detection methods.
The EPSS Model is invaluable for prioritizing patching efforts based on vulnerability exploitation likelihood. One of the primary benefits of the EPSS Model is its open-source nature, allowing for widespread access, transparency, and community contributions. Using vulnerable data to make predictions provides more accurate results than scoring systems relying solely on severity ratings. By implementing the EPSS Model, organizations can enhance their cybersecurity measures and protect their digital assets more effectively.
