Shadow Replay Box for Live Attack Fidelity
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Solution Overview
Problem
Existing technologies face challenges in reliably duplicating live attacks on software in a trusted and measurable environment, making it difficult to gather reliable data for constructing countermeasures against frequent and tailored malware attacks.
Innovation Solution
The implementation of a shadow replay system that captures a snapshot of a computer program during a potential attack, rolls it back, and relaunches it in a mirrored environment for high-fidelity replay and analysis, enabling the identification of tainted variables and monitoring of their behavior.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If attacks are analyzed after identification, then attack behavior can be studied, but by the time an attack is identified it may be too late to remediate the attack or find out additional information about the attack
Solution Approach 1:
The system performs preliminary actions by capturing snapshots of the attack in progress and rolling back to pre-attack states to preserve evidence. This allows the attack to be studied in detail without waiting for post-identification analysis, effectively performing the measurement action before the attack completes and information is lost.
2Productivity
If machine learning techniques are used to analyze attacks, then countermeasures can be created, but constructing malicious sets of behaviors is intractable given the frequency and tailored nature of attacks
Solution Approach 1:
The system creates copies of attack instances by capturing snapshots and rolling back to pre-attack states. These copies preserve the malicious behavior patterns without requiring manual construction of datasets, allowing machine learning techniques to be applied directly to real attack data while avoiding the intractable task of manually constructing malicious behavior sets.
3Reliability
If attacks are studied in a trusted environment, then reliable data can be gathered, but recreating or reverse engineering an attack takes significant effort and may be rendered fruitless when not performed in a time-sensitive manner
Solution Approach 1:
The system performs preliminary actions by capturing snapshots of attacks in progress and rolling back to pre-attack states before the attack completes. This preserves the attack evidence in a trusted environment at the moment it occurs, eliminating the need for time-consuming recreation efforts later while maintaining data reliability.
4Measurement precision
If snapshots are captured and rolled back for analysis, then high-fidelity traces can be obtained for machine learning, but the process requires mirroring execution environments and performing static analysis
Solution Approach 1:
The system introduces an intermediary shadow replay box that acts as a mediator between the attack execution environment and the analysis system. This intermediary captures snapshots, performs rollbacks, and conducts static analysis, thereby obtaining high-fidelity traces for machine learning while isolating the complexity of the analysis process from the core attack prevention system.
Data Source
AI summary
A live attack shadow replay can be performed at a shadow replay box that receives a snapshot of a computer program executed by an operating system of a device; mirrors an execution environment of the snapshot; determines a typical execution of the computer program comprising a first set of variables; performs a static analysis on the snapshot of the computer program to determine a second set of variables; determines a divergence between the first set of variables and the second set of variables; marks variables of the second set of variables that are associated with the divergence; replays a portion of the computer program corresponding to at least the snapshot; and monitors the marked variables of the second set of variables during the replaying of the portion of the computer program.