Rule Optimizer for Exploit Attack Detection Accuracy
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing exploit attack detection systems rely heavily on manual rule setting and optimization by administrators, leading to subjective performance guarantees, high human and time costs, and potential delays in rule updates, which can reduce detection accuracy and speed.
Innovation Solution
An apparatus and method for automatically optimizing detection rules using a rule optimizer that obtains and time-series analyzes log data to update existing rules, improving detection accuracy and speed without relying on administrator expertise.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual rule setting and optimization is performed by administrators, then detection rules can be customized, but detection performance cannot be objectively guaranteed and human/time costs are high
Solution Approach 1:
The system performs self-optimization by automatically analyzing log data and updating detection rules without requiring manual intervention from administrators. The rule optimizer module autonomously processes log files, identifies attack patterns, and updates rules based on observed behavior, enabling the system to improve its own detection performance objectively.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring log data, analyzing detection results, and using this information to refine and update detection rules. The rule optimizer receives feedback from actual attack detection outcomes and adjusts rules accordingly, creating a closed-loop system that objectively improves detection performance over time.
2Measurement precision
If manual rule updates are performed periodically, then rules can be maintained, but detection speed and accuracy are reduced due to delays
Solution Approach 1:
The system performs preliminary analysis of log data continuously and proactively identifies new attack patterns before they become prevalent. The rule optimizer prepares and updates detection rules in advance based on emerging trends, ensuring that the system is ready to detect new attacks immediately without waiting for periodic manual updates.
Solution Approach 2:
The system transitions from periodic manual rule updates to continuous automated optimization. The rule optimizer operates continuously, constantly analyzing log data and updating rules as needed, eliminating the gaps and delays associated with periodic updates while maintaining high detection accuracy.
3Productivity
If automated rule optimization is implemented, then detection performance is objectively improved, but system complexity increases
Solution Approach 1:
The system extracts the rule optimization functionality into a separate, dedicated rule optimizer module that operates independently from the main detection system. This extraction allows the optimization process to be isolated and managed separately, reducing the complexity burden on the core detection system while still achieving automated improvement.
Solution Approach 2:
The rule optimizer acts as an intermediary component between the detection system and the administrators. It handles the complex tasks of log analysis, pattern recognition, and rule generation automatically, shielding administrators from complexity while enabling objective performance improvement. The intermediary absorbs the computational and analytical overhead.
4Adaptability or versatility
If rule optimization depends on administrator experience, then rules can be tailored to specific needs, but detection performance varies and human costs are incurred
Solution Approach 1:
The system performs self-optimization by automatically analyzing log data and updating detection rules without requiring manual intervention from administrators. The rule optimizer module autonomously processes log files, identifies attack patterns, and updates rules based on observed behavior, enabling the system to improve its own detection performance objectively.
Solution Approach 2:
The system automatically adjusts rule parameters such as detection thresholds, priority levels, and activation conditions based on analyzed log data and observed attack patterns. This automated parameter optimization eliminates the need for administrators to manually tune rules while maintaining adaptability to specific organizational needs through data-driven adjustments.
Data Source
AI summary
An apparatus comprising a processor to execute the rule optimizer to perform a number of operations. One operation comprises obtaining 5 log data including a result of detecting an exploit attack based on a rule. Another operation comprises time-series analyzing the obtained log data to update at least some of previously applied detection rules. There is provided an apparatus for automatically optimizing a rule to improve the detection accuracy for an exploit attack in a rule-based attack detection system, and a method performed on the apparatus.