Behavioral Index for Dual-Use Tool Attack Detection
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Solution Overview
Problem
Living-off-the-land attacks using dual-use tools are difficult to detect, as they reside solely in memory and leverage pre-installed system tools, evading traditional security measures.
Innovation Solution
A computer-implemented method for knowledge-aware detection using a computing device that collects user input data, creates a behavioral index, and applies machine learning to flag malicious attacks by determining similarity levels between new and previous inputs, implementing security actions such as blocking network connectivity or quarantining the device when thresholds are exceeded.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional security measures are used to detect attacks, then detection simplicity is maintained, but detection capability against fileless malware using dual-use tools deteriorates
Solution Approach 1:
The patent introduces behavioral context data as an intermediary layer between the dual-use tool execution and security detection. This contextual information (user behavior patterns, command-line usage history, process execution sequences) serves as a mediator that enables detection of malicious intent without requiring complex analysis of the tool's core functionality, thus improving detection capability while managing system complexity
Solution Approach 2:
The patent replaces traditional signature-based detection mechanisms with machine learning models that analyze behavioral patterns. Instead of relying on predefined signatures or rule-based systems, the solution uses trained ML models to predict malicious behavior based on contextual data, substituting mechanical detection methods with intelligent pattern recognition that is more effective against fileless malware
2Measurement precision
If dual-use tools are monitored closely to detect attacks, then detection precision improves, but ease of operation for legitimate users deteriorates
Solution Approach 1:
The patent applies different levels of monitoring and analysis to different aspects of tool usage. Behavioral context data is collected and analyzed selectively based on the specific tool, user, and operational context. The system adjusts the depth and type of monitoring locally rather than applying uniform surveillance, thereby maintaining high detection precision for malicious activities while preserving ease of operation for legitimate users
Solution Approach 2:
The patent dynamically adjusts detection parameters such as threshold values, monitoring intensity, and analysis depth based on the assessed risk level and user behavior patterns. The machine learning models continuously learn from user interactions and adapt their parameters to distinguish between legitimate and malicious usage, maintaining precision without unnecessarily complicating user operations
3Measurement precision
If behavioral context data is collected to improve detection accuracy, then detection accuracy improves, but user privacy deteriorates
Solution Approach 1:
The patent extracts and analyzes only the specific behavioral context data elements that are directly relevant to detecting malicious use of dual-use tools. Rather than collecting comprehensive user data, the system selectively extracts features such as command-line parameters, process execution sequences, and timing patterns that are indicative of attack behavior, thereby improving detection accuracy while minimizing privacy intrusion
Data Source
AI summary
Knowledge-aware detection of attacks on a client device conducted with dual-use tools. A method may include obtaining dual-use tool data related to a plurality of dual-use tools; collecting from a client device, by the computing device, user input related to the use of a dual-use tool of the plurality of dual-use tools; determining that the user input contains a feature of the dual-use tool data; creating a behavioral index of the user input, the behavioral index stored on the client device; detecting new input on the client device; determining a similarity level between the user input and the new input; flagging a malicious attack on the client device based on determining that the similarity level does not satisfy a pre-determined threshold; and implementing a security action on the client device based on flagging the malicious attack.