Scalable Incident-Response Toolkit for Real-Time Anomaly Detection
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Solution Overview
Problem
Current cyber security systems are inadequate in detecting advanced cyber threats in real-time, leading to time-consuming recovery processes and potential mission failures or loss of critical services, especially in military or critical-infrastructure settings.
Innovation Solution
A Scalable Incident-response and Forensics Toolkit (SIFT) with a distributed architecture that unifies datasets from various sources into a uniform schema for real-time anomaly detection, using a data translator and anomaly detection system, enabling large-scale data aggregation and threat detection without compromising performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If a distributed architecture is used to aggregate large-scale data from multiple sources, then data aggregation capability and detection coverage are improved, but system complexity increases
Solution Approach 1:
The system divides data aggregation and analysis into distributed segments across multiple nodes. Each node collects and processes data locally before transmitting to central processing, enabling scalable data aggregation without monolithic system complexity. The framework segments data from multiple sources (network sensors, host systems, cloud services) into manageable streams that can be processed independently.
Solution Approach 2:
The framework provides a universal data translation layer that handles multiple data formats and sources through a single interface. The common data format specification serves as a universal translator between diverse data sources and the analysis engine, reducing the need for source-specific processing logic and simplifying system architecture despite handling diverse data types.
2Speed
If real-time anomaly detection is implemented to detect advanced cyber threats, then response time is improved, but computational resources and processing power requirements increase
Solution Approach 1:
The system performs preliminary data translation and normalization in advance, converting diverse data formats into a common schema before analysis. This pre-processing reduces the computational burden during real-time anomaly detection, as the analysis engine receives standardized data rather than processing multiple formats simultaneously.
Solution Approach 2:
The framework transforms data from multiple dimensions (different sources, formats, and structures) into a unified data representation. By projecting diverse data into a common dimensional space defined by the shared data format, the system enables efficient real-time analysis without requiring separate processing paths for each data source, reducing overall computational complexity.
3Adaptability or versatility
If data is converted into a common format for unified analysis, then data integration and interoperability are improved, but data processing time increases
Solution Approach 1:
The common data format acts as an intermediary layer between diverse data sources and the analysis engine. Rather than converting all data to a single format sequentially, the framework establishes the common format as an intermediate representation that enables parallel processing and efficient data exchange between components, reducing overall processing time while maintaining integration capability.
Solution Approach 2:
The system changes data parameters (format, structure, encoding) dynamically based on source requirements. The data translation layer adjusts parameters to match the common format specification, enabling efficient conversion without requiring complete data restructuring. This parameter-level transformation is more lightweight than structural reorganization, reducing processing time while achieving format unification.
4Measurement precision
If advanced anomaly detection algorithms are used to detect zero-day threats, then detection accuracy is improved, but system resource consumption increases
Solution Approach 1:
The framework extracts and isolates anomaly detection logic from the data processing pipeline, allowing specialized algorithms to operate on pre-processed data. By separating detection functions, the system can apply resource-intensive algorithms only where needed (e.g., for suspicious patterns) rather than processing all data uniformly, reducing overall resource consumption while maintaining high detection accuracy for zero-day threats.
Data Source
AI summary
Techniques for performing data analytics using anomaly detection systems and methods are disclosed. The anomaly detection system provides an incident response and monitoring solution, built for distributed processing, that streamlines cyber defense by unifying datasets, via a data translator, from sensors and tools into a uniform schema to provide real-time anomaly detection, via an anomaly detection system that may prevent malware from establishing a foothold on the network. The anomaly detection system may allow for the scalability to provide large-scale data aggregation and anomaly detection without compromising performance. The anomaly detection system may use a distributed architecture to support advanced cyber threat detection across large datasets in real-time for monitoring and rapid incident response. The anomaly detection system may leverage open protocols and interfaces to promote third-party support for development and interoperability.