Decoy Environment for Malware Intelligence Gathering
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Solution Overview
Problem
Current security mechanisms are ineffective in identifying and preventing advanced persistent threats, as malware can evade detection by delaying execution or recognizing sandbox environments, and existing methods fail to gather intelligence on attacker goals and operations.
Innovation Solution
A decoy environment is created that mimics the organization's network, allowing suspected malware to run on physical machines and interact with decoy systems, enabling tracking of malicious activities without evasion, and providing information on attacker methods and goals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a sandbox environment is used to execute suspicious code, then the organization's real resources are protected from malware, but the malware can detect the emulation environment and delay or avoid execution of attacking code
Solution Approach 1:
The patent introduces a decoy environment as an intermediary between the sandbox and the malware. This decoy environment mimics the organization's real resources (files, directories, network services) but is isolated and monitored. The malware interacts with the decoy environment instead of detecting the sandbox directly, allowing intelligence gathering while maintaining protection. The decoy environment acts as a mediator that provides realistic interaction points without exposing actual organizational resources.
2Productivity
If signature matching is used to identify malware, then known malware families can be detected, but the method cannot keep up with constantly changing and mutated malware signatures
Solution Approach 1:
The patent replaces the mechanical signature matching system with a behavioral analysis approach. Instead of comparing static code signatures, the system executes the suspicious code in a controlled decoy environment and observes its actual behavior (file operations, network communications, registry modifications). This substitution allows detection based on what malware does rather than what it looks like, making detection resilient to signature changes while maintaining high accuracy.
3Measurement precision
If machine learning and behavioral analysis are used to identify malware communication, then suspicious network traffic can be detected, but the methods trigger false positives and suffer from false negatives
Solution Approach 1:
The patent segments the security analysis system into distinct functional components: a decoy environment that provides realistic interaction points, a monitoring layer that captures behavioral data, and an analysis layer that processes the captured information. This segmentation allows each component to specialize in specific tasks, reducing false positives by focusing on concrete behavioral evidence rather than attempting comprehensive traffic analysis with complex machine learning models.
Data Source
AI summary
A system for gathering information about malware and a method of use therefor, the system comprising a working environment including physical working environment servers, physical working environment endpoints, a working environment network, a switch, and a router directing traffic between said working environment network and an external network, a decoy environment including at least one physical machine, a decoy environment server, a decoy environment endpoint, a decoy environment network and a decoy environment router, a file directing mechanism directing at least some files to the decoy environment, and a threat tracking mechanism tracking and observing actions triggered by the files in the decoy environment.