Behavioral Threat Detection Rule Compilation
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Solution Overview
Problem
Traditional malware detection techniques rely on signature-based analyses, which are ineffective against new threats until signatures are updated, leading to a slow and reactionary security solution. Additionally, these techniques introduce potential security vulnerabilities and complicate the distribution of malware definitions.
Innovation Solution
The use of behavioral threat detection systems that define and compile behavior rules based on event instructions, allowing for dynamic and platform-independent threat detection. These systems process events and associated computations to identify behaviors indicative of malicious software, using a rule virtual machine to execute compiled behavior rules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If signature-based malware detection is used, then detection accuracy for known threats is improved, but adaptability to new threats deteriorates
Solution Approach 1:
The patent transitions from static signature-based detection to dynamic behavior-based detection. The system continuously monitors and analyzes runtime behavior of executables, adapting to new threats in real-time without requiring pre-defined signatures. This dynamic approach allows the system to detect both known and unknown malware based on their behavioral patterns during execution.
Solution Approach 2:
The patent changes the detection parameters from static file signatures to dynamic behavioral parameters. Instead of matching fixed patterns in files, the system monitors runtime parameters such as process creation, file access patterns, registry modifications, and network connections. This parameter transformation enables detection of new threats based on their behavioral characteristics rather than requiring updated signatures.
2Ease of manufacture
If traditional malware detection techniques are used, then implementation simplicity is improved, but security vulnerability risk increases
Solution Approach 1:
The patent introduces a behavior analysis engine as an intermediary layer between the executable and the detection system. This intermediary monitors and analyzes behavior without directly interacting with potentially malicious code, reducing the attack surface. The system uses sandboxing and virtualized environments as intermediaries to safely observe executable behavior, thereby reducing security vulnerabilities while maintaining detection capability.
3Reliability
If logic-based malware detection approaches are used, then detection capability is improved, but system complexity increases
Solution Approach 1:
The patent segments the malware detection system into distinct modular components: a behavior monitoring module that collects runtime data, a behavior analysis engine that processes the data, and a detection module that identifies threats. This segmentation allows each component to perform its function independently, managing complexity while maintaining comprehensive detection capability through coordinated operation of specialized modules.
Data Source
AI summary
Examples of the present disclosure describe systems and methods for behavioral threat detection definition. In an example, a behavior rule comprising a set of rule instructions is used to define one or more events indicative of a behavior. For example, a set of events from which one event must be matched may be defined or a set of events from which all events must be matched may be defined. In some examples, events are matched based on an event name or type, or may be matched based on one or more parameters. Exact and/or inexact matching may be used. The set of rule instructions ultimately specifies one or more halt instructions, thereby indicating that a determination as to the presence of the behavior has been made. Example determinations include, but are not limited to, a match determination, a non-match determination, or an indication that additional monitoring should be performed.