Baseline Calculation for Firewalling in Tailored Sandboxes
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Solution Overview
Problem
Current sandboxing methods lack adaptation to individual computer systems, leading to inefficiencies in threat detection and false positives, as they do not account for specific software versions, usage patterns, and network context.
Innovation Solution
Implementing tailored sandboxes that perform baseline calculations using local data to calibrate and prioritize sandbox parameters, allowing for dynamic adjustments based on actual system behavior and resource allocation, thereby enhancing threat detection and reducing false positives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If general sandboxing methods are used to detect threats, then broad coverage of potential attacks is achieved, but false positives increase and detection accuracy decreases for specific systems
Solution Approach 1:
The patent performs preliminary actions by collecting baseline data about normal system behavior before threat detection begins. This includes gathering information about installed software, hardware configurations, and typical operational patterns. By establishing this baseline in advance, the sandbox can later compare observed behaviors against known normal patterns, significantly improving detection accuracy while reducing false positives.
Solution Approach 2:
The system implements feedback mechanisms where detection results and system responses are continuously fed back into the baseline calculation process. When new threats are identified or system configurations change, the baseline is updated accordingly. This closed-loop feedback ensures the sandbox adapts to individual systems over time, maintaining high detection accuracy while accounting for system-specific variations.
2Measurement precision
If tailored sandboxes with baseline calculations are implemented, then threat detection accuracy improves, but system complexity and resource requirements increase
Solution Approach 1:
The patent segments the sandbox system into distinct functional modules: baseline data collection, baseline calculation, behavior monitoring, and threat detection. Each module performs a specific function and can be independently configured or optimized. This segmentation reduces overall system complexity by making each component manageable and allowing parallel processing of different aspects of threat detection.
Solution Approach 2:
The system dynamically adjusts operational parameters based on the calculated baseline and observed system state. Rather than using fixed thresholds, the sandbox modifies detection sensitivity, sampling rates, and resource allocation according to system-specific characteristics. This parameter adaptation maintains high detection accuracy while optimizing resource usage and reducing unnecessary complexity.
3Measurement precision
If comprehensive baseline data is collected for all systems, then detection precision improves, but data processing time and computational resources increase
Solution Approach 1:
The patent applies partial action by selectively collecting baseline data based on system priorities and risk profiles. Rather than gathering exhaustive data from all systems uniformly, the system focuses on collecting detailed baselines for high-value or high-risk systems while using lighter-weight baselines for lower-priority systems. This approach maintains detection precision for critical systems while reducing overall data processing time and resource consumption.
Data Source
AI summary
A method, computer program product, and apparatus for performing baseline calculations for firewalling in a computer network is disclosed. The method involves defining a reference group for an executed software program, measuring signals in the reference group, measuring signals of the program, computing a distance between the signals of the program and the signals of the reference group, and taking an action if the computed distance deviates from a norm mode. The distance can be computed using a similarity matrix or other method. Measuring the program comprises observing behaviors of the program, collecting and analyzing data, comparing the data to baselines of the reference group, and comparing the behaviors of the program across a previous execution of the program. In cases where a program is known to be malicious, a reference group is not needed and a sandbox can be tailored just by copying the environment of the actual system.