Stateful Rule Generation Using DSL for Behavior Threat Detection
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Solution Overview
Problem
Conventional cybersecurity platforms face challenges in efficiently generating and deploying stateful behavioral rules to effectively detect and prevent evolving cyber threats, particularly due to cumbersome toolchains that require advanced programming skills and are inefficient in adapting to new threat information.
Innovation Solution
The development of tools and techniques that allow for the generation of high-level representations of stateful rules in a domain-specific programming language, which are then compiled and executed on stateful rule processing engines, enabling dynamic access control and improved threat detection without requiring advanced programming expertise.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional static IOC-based techniques are used for threat detection, then known threats can be quickly detected, but new threats (zero-day, polymorphic, metamorphic) cannot be detected
Solution Approach 1:
The patent transitions from static IOC-based detection to dynamic behavior-based detection using state machines. The system monitors sequences of events and state transitions rather than relying on fixed signatures, enabling detection of new threats through their behavioral patterns rather than static characteristics.
Solution Approach 2:
The system changes the detection parameters from static attributes (file hashes, signatures) to dynamic behavioral parameters (event sequences, state transitions, frequency of operations). This allows detection of threats that modify their static characteristics while maintaining consistent behavioral patterns.
2Adaptability or versatility
If behavior-based threat detection is implemented, then new threats can be detected, but the toolchain becomes cumbersome and requires advanced programming skills
Solution Approach 1:
The patent introduces an intermediary compilation step that translates high-level, human-readable behavior descriptions into executable detection rules. This intermediary layer allows security analysts to define threats using natural language or simplified syntax rather than complex programming, bridging the gap between analytical capability and implementation complexity.
Solution Approach 2:
The system enables automated generation and compilation of detection rules from high-level specifications. The compilation process automatically handles the complex transformations needed to create executable state machine representations, eliminating the need for manual programming while maintaining behavioral detection capabilities.
3Measurement precision
If complex stateful rules are generated manually, then precise threat detection is possible, but the process is time-consuming and inefficient
Solution Approach 1:
The patent performs preliminary compilation of high-level rule specifications into optimized executable formats before deployment. This advance processing prepares the detection logic in advance, allowing rapid deployment and execution without sacrificing the precision of complex stateful behavior definitions.
Solution Approach 2:
The system creates compiled representations (copies) of the original high-level rules that can be efficiently executed. These compiled copies preserve the precise logic of the original specifications while enabling faster processing and deployment across multiple endpoints.
Data Source
AI summary
Improved tools and techniques for generating stateful rules for behavior-based threat detection enable threat analysts, who do not have advanced computer programming skills, to quickly and easily generate high-level representations of stateful behavioral rules, which are then compiled into a format suitable for execution by a stateful rule processing engine. In some examples, the high-level representations of stateful rules are coded in a high-level, domain specific language (DSL). The DSL may provide high-level primitives suitable for (1) expressing sequences of attack behaviors, (2) tagging computational entities (e.g., threads, processes, applications, systems, users, etc.) with states (e.g., user-defined states), and/or (3) performing operations on endpoint nodes (e.g., reporting activity, blocking activity, terminating processes, etc.).