IDS Alert Analysis Using Association Rules
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Solution Overview
Problem
Intrusion Detection Systems (IDS) face challenges in achieving low false positive rates due to the categorical nature of security alert data and varying attributes, making traditional supervised learning methods difficult, and there is a need for effective methods to improve performance and analyze IDS alert data efficiently.
Innovation Solution
A method and system that applies association rules to IDS alert data, processes analyst feedback to generate new association rules, and outputs patterns within the data to identify and eliminate false positives, using a machine learning algorithm to cluster variables and determine association rules based on purity, which are then applied back to the data for improved analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional supervised learning methods (decision tree, neural network, RIPPER) are applied to IDS alert data, then the system can process security alerts, but the categorical nature of the data and varying attributes make these methods difficult to apply effectively
Solution Approach 1:
The patent transforms the categorical nature of IDS alert data into a suitable format for association rule mining by changing the parameter representation. Instead of using traditional supervised learning parameters that struggle with categorical data, the system applies association rule mining techniques that naturally handle categorical variables, thereby resolving the adaptability issue while maintaining methodological simplicity
2Measurement precision
If analysts manually analyze IDS alert data for long periods, then they can identify true positives, but the large number of false positives causes true negatives to be missed and reduces analysis efficiency
Solution Approach 1:
The system performs preliminary filtering and prioritization of alerts using association rule mining before analyst review. By pre-processing the alert data to identify and flag high-probability true positives and eliminate obvious false positives, the system prepares the data in advance for more efficient analyst analysis, thereby improving both accuracy and productivity
Solution Approach 2:
The system implements feedback mechanisms where analyst decisions on alert classifications are used to refine and improve the association rules over time. This continuous learning process enhances the system's ability to distinguish true positives from false positives, improving measurement precision while reducing the manual analysis burden
3Reliability
If the false positive rate is reduced in IDS, then more true positives can be identified, but achieving low false positive rates is difficult under current IDS state
Solution Approach 1:
The patent segments the alert analysis process into distinct phases: initial association rule-based filtering, prioritization of high-risk alerts, and focused analyst review. By dividing the complex task of false positive reduction into manageable segments, the system achieves improved reliability without requiring overly complex centralized processing
Data Source
AI summary
A system and method for analyzing Intrusion Detection System (IDS) alert data associated with a computer network is described. The method includes applying first association rules to obtained IDS alert data associated with a computer network and processing the obtained IDS alert data with the first association rules. Analyst feedback data associated with the processed obtained IDS alert data is received, and a training data set from the analyst feedback data is received. New association rules are determined based upon the training data set, and the new association rules are outputted to a display of a computing device. Outputting the new association rules may include outputting patterns within the IDS alert data of false positive alerts. The new association rules may be applied back to the obtained IDS alert data.