Adaptive Greylist Processing for Cybersecurity
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Solution Overview
Problem
Existing cybersecurity methods are susceptible to sophisticated cyberattacks, including 'Low and Slow' and 'Spread' attacks, and often incorrectly identify legitimate users as false positives, leading to unnecessary access denials and high support volumes, while requiring significant resources and latency.
Innovation Solution
An adaptive greylist processing system that evaluates login pairings based on success metrics, adding IP addresses to a greylist only if they meet a minimum success threshold, and adjusts this threshold based on factors like ISP, wireless carrier, geolocation, and IP version, to provide conditional access and reduce false positives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing cybersecurity methods use black lists and white lists to sort logon attempts, then known bad-actor IP addresses are blocked and authorized users are granted access, but the system becomes susceptible to sophisticated cyberattacks like Low and Slow attacks and Spread attacks
Solution Approach 1:
The system dynamically adjusts the greylist threshold based on multiple factors including ISP reputation, geolocation risk levels, wireless carrier information, and IP version characteristics. This dynamic adaptation allows the system to respond to varying threat levels and attack patterns, making it resilient against sophisticated cyberattacks while maintaining legitimate access
Solution Approach 2:
The system changes the success metric threshold parameter based on external factors such as ISP reputation scores, geolocation risk assessments, and network carrier reliability. By adjusting this critical parameter dynamically, the system optimizes its security response to different attack vectors while reducing false positives for legitimate users
2Reliability
If the system adds IP addresses to greylist based on login success metrics, then Low and Slow attacks are countered, but legitimate users may be incorrectly identified as false positives
Solution Approach 1:
The greylist serves as an intermediary layer between the whitelist and blacklist, providing conditional access for IP addresses with moderate risk profiles. By introducing this intermediate category with dynamic threshold adjustment, the system reduces false positives by allowing legitimate users with temporarily elevated risk scores to still access services while maintaining enhanced monitoring
Solution Approach 2:
The system applies different success metric thresholds to different IP addresses based on their specific characteristics such as ISP reputation, geolocation, and carrier information. This localized quality adjustment ensures that each IP address is evaluated according to its specific risk profile, reducing false positives for legitimate users while maintaining strict security for high-risk sources
3Measurement precision
If the system evaluates multiple factors like ISP, geolocation, and carrier information, then false positives are reduced, but processing resources and latency increase
Solution Approach 1:
The system performs preliminary evaluations using readily available data such as IP address lookup tables containing ISP and geolocation information. By pre-processing and caching this data, the system reduces the computational burden during actual login evaluations, maintaining high measurement precision while minimizing processing resource consumption and latency
Data Source
AI summary
Adding an internet location to a greylist includes receiving a login pairing that includes login credentials and an internet location that the login credentials are received from. A successful login number of prior successful logins associated with the login pairing is determined and the internet location may be added to the greylist based at least in part on the successful login number.