Network Traffic Policy Generation for Protocol-Based Anomaly Detection
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Solution Overview
Problem
Existing computer security systems lack efficient methods for automated security policy generation and anomaly detection in network traffic, leading to potential unauthorized access and disruptive traffic.
Innovation Solution
A system and method that utilizes a machine learning engine to analyze network traffic data, identify communication protocols, restructure the data based on detected protocols, and generate enforceable security policies, while performing anomaly detection by comparing live traffic against learned 'known good' data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated security policy generation is implemented, then security management efficiency is improved, but system complexity increases
Solution Approach 1:
The system performs self-learning by automatically analyzing network traffic patterns and generating security policies without human intervention. The machine learning engine continuously monitors traffic, identifies anomalies, and updates policies autonomously, eliminating the need for manual security configuration while improving efficiency.
Solution Approach 2:
Manual security policy creation and anomaly detection processes are replaced with automated machine learning algorithms. The system uses computational models to analyze traffic patterns, detect threats, and generate policies, substituting human expertise with automated intelligent systems.
2Reliability
If comprehensive anomaly detection is performed, then security detection capability is improved, but processing time increases
Solution Approach 1:
The system pre-processes and stores normal traffic patterns during a learning phase before deployment. By establishing baseline behavior patterns in advance, the system can quickly compare live traffic against these pre-established norms, enabling fast anomaly detection without extensive real-time analysis.
Solution Approach 2:
The machine learning model transforms raw network traffic data into standardized features and parameters for comparison. By converting diverse traffic types into uniform parameter sets, the system enables efficient processing while maintaining comprehensive detection capability across different protocols and traffic patterns.
3Manufacturing precision
If protocol-specific restructure rules are applied, then data processing accuracy is improved, but rule complexity increases
Solution Approach 1:
The system divides network traffic into distinct segments based on protocol types (HTTP, FTP, SMTP, etc.). Each protocol segment is processed using protocol-specific restructure rules, allowing accurate handling of each protocol while managing complexity through modular organization of rules for different traffic types.
Data Source
AI summary
A method includes receiving first network traffic data having a plurality of data packets. A representation of each data packet from the plurality of data packets is stored in a first data store, to produce a set of training data. A plurality of communication protocols associated with the set of training data is detected. A set of rules based on the plurality of communication protocols is determined. The set of rules includes domain-specific information associated with each communication protocol detected. Training data from the set of training data is restructured according to the set of rules to produce processed data. The processed data is stored as non-malicious data in a second data store. A security policy is generated based on the processed data.