IP Reputation Prediction via Query Pattern Matching
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Solution Overview
Problem
Current methods for combating email spam, such as honey pots, are inadequate in identifying all malicious computers and predicting future spam attacks, as they rely on interaction with honey pots and hypothetical models, leading to inefficiencies and false positives.
Innovation Solution
A prediction technique that analyzes daily query patterns of unknown IP addresses by matching them against behavior patterns of known good and bad IP addresses, using normalization and suffix trees to identify reputations, thereby improving detection rates and reducing false positives without the need for additional honey pots.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If honey pots are used to detect spammer activity, then spammer IP addresses can be revealed and bulk spam captured, but not all malicious computers can be identified and future spam attacks cannot be predicted
Solution Approach 1:
The system performs preliminary analysis by collecting and analyzing query patterns from known good and bad IP addresses before encountering new unknown IP addresses. This pre-established behavioral baseline enables the system to predict the reputation of unknown IP addresses proactively, rather than reacting only after honey pot interactions occur.
Solution Approach 2:
The system creates a copy of behavioral patterns from known good and bad IP addresses to use as reference models. By copying and comparing these established patterns against unknown IP address behaviors, the system can predict reputations without requiring direct interaction with each unknown address in honey pots.
2Object-generated harmful factors
If traditional anti-spam techniques are used, then spam filters and blacklists can delete spam e-mail, but aggressive spammers can use deluge of spam to attack companies and identify malicious computers is difficult
Solution Approach 1:
The system uses feedback from actual customer e-mail server query patterns to continuously refine and improve the behavioral models of good and bad IP addresses. This real-world feedback loop enables more accurate prediction of unknown IP address reputations, making malicious computer identification progressively easier as the system learns from actual spam attack patterns.
Solution Approach 2:
The system replaces the mechanical honey pot trap mechanism with a data-driven pattern recognition approach. Instead of relying on physical trap interactions to reveal spammer identities, the system substitutes this with automated analysis of query pattern data to predict malicious behavior, thereby reducing the difficulty of detecting and measuring malicious computers.
3Reliability
If more honey pots are deployed to improve detection coverage, then more malicious computers can be identified, but costs increase and efficiency decreases
Solution Approach 1:
The system copies and analyzes query pattern data from existing e-mail server interactions to create behavioral models, eliminating the need to deploy additional physical honey pots. This data copying approach maintains identification accuracy while significantly reducing deployment complexity and infrastructure requirements.
Solution Approach 2:
The system substitutes the mechanical deployment and maintenance of additional honey pots with a software-based pattern recognition system that processes existing query data. This substitution reduces device complexity by replacing physical infrastructure expansion with data processing capability enhancement.
4Loss of time
If hypothetical models are used for spam detection, then detection can be performed without real-time interaction, but false positives increase and detection rate decreases
Solution Approach 1:
The system continuously refines its hypothetical models by incorporating feedback from actual customer query patterns and e-mail server behaviors. This feedback mechanism allows the system to maintain rapid detection capability while progressively improving accuracy by adjusting models based on real-world outcomes, reducing false positives over time.
Solution Approach 2:
The system dynamically changes the parameters and characteristics of its behavioral models based on accumulating query pattern data. By adapting model parameters to reflect actual observed behaviors rather than static hypothetical scenarios, the system improves detection accuracy while maintaining the speed advantage of automated pattern matching.
Data Source
AI summary
Daily query counts for e-mail messages sent from a number of IP addresses having unknown reputations are collected and logged, and optionally plotted. The logged query count data may optionally be normalized. The normalized query count data may also be plotted. The normalized data is divided into regions (numerically or graphically). Next, the divided regions are tagged (symbolically or graphically) with unique, symbolic identifiers such as letters, numbers, symbols or colors. Patterns for each unknown IP address are formed based upon the tagged regions. Common good and bad patterns are also identified for known good and bad IP addresses. The reputation of these unknown IP addresses are then predicted using these identified good and bad patterns using a suffix tree (for example). Finally, an output identifying the determined reputations of these unknown IP addresses is generated and output.