Smart DNS Whitelisting via Dynamic Threat Filtering
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Solution Overview
Problem
Existing DNS security whitelisting approaches are prone to errors, are time-consuming, and result in stale or overly broad lists, as they rely on manual processes and publicly available lists that may include malicious domains, failing to adapt to the evolving internet environment and specific customer data environments.
Innovation Solution
A system and process for smart whitelisting that generates context-specific, data-driven whitelists using DNS event data and threat data, filtered to exclude malware, and dynamically updated using statistical classifiers like Bayesian inference models, to create adaptive and effective whitelists that adjust to changing threat levels and customer environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual whitelisting processes are used, then implementation simplicity is maintained, but accuracy and adaptability deteriorate due to errors and stale lists
Solution Approach 1:
The system automatically generates and maintains whitelists by monitoring DNS query patterns and domain popularity metrics without requiring manual intervention. The whitelisting mechanism serves itself by continuously collecting data, analyzing trends, and updating the whitelist automatically, eliminating human error while maintaining accuracy.
Solution Approach 2:
The system implements continuous feedback loops where DNS query data is collected, analyzed against threat intelligence feeds, and used to dynamically adjust the whitelist. This feedback mechanism ensures the whitelist remains current and accurate by constantly comparing actual usage patterns with known threat data.
2Ease of manufacture
If publicly available domain lists are used for whitelisting, then ease of implementation is improved, but security reliability deteriorates due to inclusion of malicious domains
Solution Approach 1:
The system introduces threat intelligence feeds as an intermediary layer between public domain lists and the final whitelist. These feeds act as a filter that cross-references proposed whitelist entries against known malicious domains, blocking potentially harmful entries while allowing legitimate ones to pass through to the whitelist.
Solution Approach 2:
The system performs preliminary filtering and validation of domain candidates before adding them to the whitelist. By pre-screening domains against multiple criteria including threat intelligence data, domain age, and popularity metrics, the system prevents malicious domains from entering the whitelist in the first place.
3Adaptability or versatility
If static whitelists are maintained, then system simplicity is preserved, but adaptability to evolving threats deteriorates
Solution Approach 1:
The whitelist transitions from a static structure to a dynamic one that automatically adjusts based on real-time DNS query patterns and threat intelligence updates. The system continuously monitors domain popularity and threat levels, automatically adding or removing domains from the whitelist without manual intervention, making it adaptable to evolving threats while minimizing time loss.
4Reliability
If comprehensive threat data filtering is applied, then security protection is improved, but false positives increase leading to larger whitelist sizes
Solution Approach 1:
The system dynamically adjusts filtering parameters and thresholds based on the specific customer environment and threat landscape. By modifying sensitivity levels, domain age requirements, and popularity thresholds according to contextual factors, the system maintains high security protection while minimizing false positives that would unnecessarily inflate whitelist size.
Data Source
AI summary
Techniques for smart whitelisting for Domain Name System (DNS) security are provided. In some embodiments, a system/process/computer program product for smart whitelisting for DNS security in accordance with some embodiments includes receiving a set of network related event data, wherein the set of network related event data includes Domain Name System (DNS) related event data; receiving a set of network related threat data, wherein the set of network related threat data includes DNS related threat data; and generating a whitelist using the set of network related event data and the set of network related threat data, wherein the whitelist includes a subset of network domains included in the DNS related event data based on a data driven model of the DNS related event data and the DNS related threat data.