Cybersecurity Data Pipeline Baseline Comparison
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Solution Overview
Problem
Existing vulnerability detection techniques in cybersecurity environments are reactive, inefficient, and costly, struggling to scale with the increasing number of threats and requiring methodologies or systems to gather data, which are impractical for companies with limited security budgets.
Innovation Solution
A method and system for identifying vulnerabilities by receiving raw data from assets, comparing it to a baseline profile, filtering out expected differences, and using a pattern recognition module to detect malicious modifications, which can implement threat mitigation procedures without the need for extensive data collection systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing vulnerability detection techniques are used, then malware or malicious activity can be detected, but the detection is reactive and only occurs after the asset is already infected
Solution Approach 1:
The system performs preliminary actions by establishing a baseline profile of normal asset behavior before malicious activity occurs. This baseline serves as a reference point for detecting deviations, enabling proactive identification of vulnerabilities and malicious modifications before they result in full infections or attacks.
Solution Approach 2:
The system implements continuous feedback by comparing real-time raw data from assets against the established baseline profile. This feedback mechanism enables the system to detect deviations immediately when they occur, transforming reactive detection into a near-real-time monitoring system that can alert administrators to potential threats before they escalate.
2Reliability
If comprehensive data collection systems are implemented to monitor all assets, then vulnerability detection capability improves, but the cost and complexity of the system increases significantly
Solution Approach 1:
The system extracts only the essential and relevant data points from assets needed for vulnerability detection, rather than collecting and analyzing all possible data. By focusing on specific parameters that indicate vulnerabilities and malicious modifications, the system achieves effective monitoring with reduced data collection overhead and lower computational requirements.
Solution Approach 2:
Instead of implementing complex centralized monitoring systems on every asset, the system creates simplified baseline profiles that capture the essential characteristics of each asset. These baseline copies serve as lightweight references that can be stored and compared without requiring the full complexity of the original asset data, reducing system complexity while maintaining detection capability.
3Loss of information
If existing monitoring techniques request and gather required data from networks, then information about network operation is obtained, but the methodology is not practical for companies with limited security budgets
Solution Approach 1:
The system implements self-service by automatically collecting raw data from assets and performing baseline establishment and comparison without requiring external security methodologies or systems. Assets essentially serve themselves by providing their own operational data, which the system then processes to identify vulnerabilities, eliminating the need for complex external data collection infrastructure.
Solution Approach 2:
The system applies partial action by gathering only the specific data needed for vulnerability detection rather than comprehensive network monitoring data. This selective data collection approach reduces the burden on companies with limited budgets, as it requires minimal infrastructure changes and can be implemented with existing asset data without needing extensive security budgets for comprehensive monitoring systems.
Data Source
AI summary
Disclosed herein are methods, systems, and processes for generating, configuring, and implementing a data collection and analytics (DCA) pipeline to optimize the identification of anomalous or vulnerable computing assets and/or anomalous or vulnerable computing asset behavior in cybersecurity computing environments. Raw data from an agent executing on a computing asset is received. A baseline profile or a gold image associated with the computing asset is also received. A difference or delta between the raw data and the baseline profile or the gold image is identified, and an output providing context relating to the difference is generated. The difference relates to a keyed property that is common between the raw data and the base profile or the gold image, and the difference is further filtered to reduce noise in the output.