Cross-Protocol IP Reputation via Machine Learning
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Solution Overview
Problem
Protocol-specific IP reputation systems often lead to over-blocking and false positives when applied generically across different protocols, as they are not adaptable and can result in unintended blocking of legitimate communications.
Innovation Solution
A system that leverages cross-protocol analysis using machine learning to generate a generic IP reputation by combining behavioral data from multiple protocols, such as HTTP and email, to improve accuracy and reduce error rates, employing a Random Forest model for classification and confidence scoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If protocol-specific IP reputation systems are used, then protection against malicious communications is improved, but false positives and over-blocking increase
Solution Approach 1:
The patent combines multiple protocol-specific reputation data sources (HTTP, email, DNS, FTP, SSH, Telnet, SMTP, POP3, IMAP, IRC, and generic network protocols) into a unified IP reputation assessment system. By merging these diverse data sources, the system achieves more accurate and reliable reputation evaluation while reducing false positives that occur when using single-protocol systems in isolation.
Solution Approach 2:
The patent creates a universal IP reputation system that functions across multiple communication protocols simultaneously. The system evaluates IP addresses using a standardized reputation model that can be applied universally to different protocols, enabling consistent protection effectiveness without the need for separate protocol-specific systems.
2Adaptability or versatility
If protocol-specific IP reputation systems are applied generically across different protocols, then protection coverage is improved, but false positives increase
Solution Approach 1:
The patent implements a universal reputation model that can be applied across multiple protocols while maintaining accuracy. The system uses protocol-agnostic features and evaluation criteria that adapt to different communication contexts, allowing the same reputation system to function effectively for HTTP, email, DNS, and other protocols without generating false positives.
Solution Approach 2:
The patent adjusts evaluation parameters and thresholds based on protocol-specific characteristics while maintaining a unified reputation framework. By dynamically modifying parameters according to the communication protocol being analyzed, the system achieves both broad protocol coverage and high reputation assessment accuracy.
3Measurement precision
If multiple protocol-specific data sources are combined, then IP reputation accuracy is improved, but system complexity increases
Solution Approach 1:
The patent merges multiple protocol-specific data sources and evaluation systems into a single unified IP reputation assessment platform. By consolidating these resources, the system achieves high reputation accuracy through comprehensive multi-protocol analysis while avoiding the complexity that would result from maintaining separate independent systems for each protocol.
Data Source
AI summary
Methods, apparatus, systems and articles of manufacture for producing generic Internet Protocol (IP) reputation through cross-protocol analysis are disclosed. An example apparatus includes a data collector to gather a first data set representing IP telemetry data for a first protocol, the data collector to gather a second data set representing IP telemetry data for a second protocol different from the first protocol. A label generator is to generate a training data set based on records in the first data set and the second data set having matching IP addresses, the training data set to include combined label indicating whether each of the respective matching IP addresses is malicious. A model trainer is to train a machine learning model using the training data set. A model executor is to, responsive to a request from a client device, execute the machine learning model to determine whether a requested IP address is malicious.