Call Stack Exploit Detection for Low-False-Positive Security
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Solution Overview
Problem
Current security measures fail to detect sophisticated security exploits at an early stage due to high false positive rates, allowing malicious code to gain a foothold on devices, leading to potential damage and difficulty in removal.
Innovation Solution
Implementing a security agent with a call stack actor (CSA) to analyze the computing context and inferentially detect exploits by analyzing call stacks, reducing false positives and enabling preventative actions without user input.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current security applications scan for exploit signatures, then known exploits can be detected and quarantined, but sophisticated security exploits are missed and false positive rates are high
Solution Approach 1:
The patent segments the exploit detection process into multiple analysis dimensions: call stack analysis, memory access pattern analysis, and behavioral sequence analysis. By dividing the detection task into these separate analytical components, the system can evaluate exploits from multiple angles without relying on a single signature-matching approach, thereby improving detection accuracy while reducing false positives.
Solution Approach 2:
The patent transitions from traditional signature-based detection to a multi-dimensional analysis approach by examining call stacks, memory access patterns, and behavioral sequences. This dimensional shift allows the system to detect sophisticated exploits that lack known signatures while establishing more reliable detection criteria that reduce false positives through correlated evidence across multiple dimensions.
2Reliability
If security applications take action only after exploit gains foothold, then false positive alerts are reduced, but damage and difficulty in removing exploit increase
Solution Approach 1:
The patent implements preliminary action by detecting exploit attempts at the earliest stage through analysis of call stacks and memory access patterns, before the exploit can establish a foothold. The system proactively identifies suspicious behaviors and takes preventive measures by blocking or quarantining the exploit early in its execution cycle, thereby preventing damage while maintaining reliability through multi-factor analysis.
Solution Approach 2:
The patent applies preliminary anti-action by preparing and executing countermeasures against exploit attempts before they can cause harm. The security system preemptively blocks suspicious call stack patterns and memory access sequences, neutralizing threats at the inception stage rather than responding after damage occurs, thus reducing harmful factors while maintaining detection reliability.
3Reliability
If security applications alert user instead of taking action, then false positives are avoided, but exploit can cause further damage
Solution Approach 1:
The patent implements self-service by enabling the security system to automatically analyze and respond to exploit attempts without requiring user intervention. The system autonomously evaluates call stack patterns, memory access behaviors, and sequential operations to make detection decisions, improving response time while maintaining reliability through sophisticated automated analysis that reduces false positives.
Solution Approach 2:
The patent changes the detection parameters from simple signature matching to complex multi-factor analysis including call stack depth, memory access patterns, and behavioral sequences. This parameter transformation enables the system to automatically distinguish between benign and malicious activities with high accuracy, allowing confident automated responses without user input while maintaining detection reliability.
Data Source
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AI summary
A security agent implemented on a monitored computing device is described herein. The security agent is configured to detect an action of interest (AoI) that may be probative of a security exploit and to determine a context in which that AoI occurred. Based on that context, the security agent is further configured to decide whether the AoI is a security exploit and can take preventative action to prevent the exploit from being completed.