Auto-Generating Attack Detection Rules via Network Traffic Analysis
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Solution Overview
Problem
Network-based Attack Detection Systems (ADS) face challenges in timely updating decision rule databases to effectively detect new threats and reducing false positives, as existing systems rely on conventional syntax and manual rule updates, which are inefficient in rapidly evolving computer networks and malware landscapes.
Innovation Solution
The auto-generation of decision rules for attack detection feedback systems, where sensors monitor network traffic, extract metadata, and centrally aggregate information to create new signatures and rules, enabling automated updates and reducing false positives by analyzing communication sessions across multiple protocols and protocols within the OSI model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual rule updates are used to maintain decision rule databases, then rule accuracy can be controlled, but the system cannot keep up with rapidly evolving threats and the update process is inefficient
Solution Approach 1:
The system automatically generates decision rules by analyzing network traffic data and threat patterns without requiring manual intervention. The ADS autonomously updates its decision rule database by extracting patterns from monitored traffic, enabling self-service maintenance that keeps pace with evolving threats while maintaining detection reliability
Solution Approach 2:
The system implements a feedback mechanism where detection results and traffic analysis are continuously fed back into the rule generation process. This closed-loop approach allows the system to learn from actual traffic patterns and threat encounters, automatically refining and updating rules based on real-world performance data
2Measurement precision
If conventional syntax and manual methods are used for rule creation, then rule precision can be maintained, but the system complexity increases and automation is limited
Solution Approach 1:
The system replaces manual mechanical rule creation processes with automated data-driven generation. Instead of analysts manually crafting rules based on conventional syntax, the system automatically analyzes network traffic patterns and generates rules algorithmically, substituting human manual work with computational processes that maintain precision while enabling full automation
Solution Approach 2:
The system extracts and copies effective rule patterns from analyzed traffic data and threat intelligence sources. By identifying recurring malicious patterns and copying their structural characteristics into new rules, the system maintains detection precision while automating the rule creation process through pattern replication rather than manual construction
Data Source
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AI summary
There is provided a method for auto-generation of decision rules for attack detection feedback systems. The method is executed on a server. The method comprises: receiving at least one event from an event database, the event database having been generated from data obtained by at least one sensor; analyzing the at least one event to determine whether the at least one event belongs to a class of malware control center interactions; if the at least one event belongs to the class of malware control center interactions, extracting at least one attribute from the at least one event; generating decision rules using the at least one attribute; and saving the decision rules; saving the decision rules, the decision rules being instrumental in updating what type of further data is obtained by the at least one sensor based on the decision rule.