Neural Network Ensemble for Cyber Threat Detection
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Solution Overview
Problem
Current cybersecurity systems face challenges in detecting and responding to sophisticated cyber threats due to the lack of a priori knowledge about new or modified malware, and existing security information and event management (SIEM) systems struggle to unify and analyze disparate data from various sources, leading to inefficiencies in generating countermeasures and enhancing security postures.
Innovation Solution
A method that unifies unstructured, unlabelled, and fragmented real-time and non-real-time cyber threat data from multiple sources into a massive analytical neural network ensemble, using unsupervised and supervised machine learning techniques to generate reinforced learning assertion functions, which are then used to enhance the security posture of cybersecurity surveillance systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If signature-based malware detection techniques are used, then detection accuracy for known threats is improved, but the system becomes ineffective against new or modified malware without a priori knowledge
Solution Approach 1:
The system performs preliminary actions by continuously collecting and analyzing cyber threat data from multiple sources to build a comprehensive database of threat patterns, indicators, and behaviors before new malware emerges. This advance preparation enables the system to detect novel threats through pattern recognition rather than requiring pre-existing signatures.
Solution Approach 2:
The system implements feedback mechanisms where detection results, false positives, and emerging threat patterns are continuously fed back into the machine learning models. This closed-loop feedback allows the system to adapt and refine its detection capabilities in real-time, improving both accuracy and adaptability to new threats.
2Adaptability or versatility
If multiple SIEM systems from different vendors are deployed, then coverage of security monitoring is improved, but data compatibility and integration become problematic
Solution Approach 1:
The system achieves universality by implementing a vendor-agnostic architecture that can ingest and process security data from multiple SIEM systems and diverse sources simultaneously. The machine learning framework is designed to handle heterogeneous data formats and protocols, enabling seamless integration without requiring vendor-specific implementations.
Solution Approach 2:
The system acts as an intermediary layer between multiple SIEM systems and the analysis engine. This mediator component standardizes data from various sources into a unified format, translating vendor-specific data structures into a common representation that the machine learning models can process effectively.
3Measurement precision
If manual review of security alerts is performed, then detection precision is improved, but response time and productivity are reduced
Solution Approach 1:
The system implements self-service capabilities through automated machine learning models that independently analyze security alerts, identify threat patterns, and generate detection decisions without requiring manual intervention. The system serves itself by continuously learning from data and automatically adapting to new threat types, maintaining high precision while eliminating manual review bottlenecks.
Solution Approach 2:
The system dynamically adjusts analysis parameters and detection thresholds based on learned patterns and contextual information. By changing parameters adaptively rather than using fixed manual rules, the system maintains high detection precision while operating at automated speeds, resolving the trade-off between precision and productivity.
4Loss of information
If extensive cyber threat data is collected from multiple sources, then analysis comprehensiveness is improved, but data processing time and computational resources increase
Solution Approach 1:
The system extracts only the most relevant and discriminative features from extensive cyber threat data using machine learning techniques. Rather than processing all raw data, the system identifies and extracts key indicators, patterns, and characteristics that are most predictive of threats, significantly reducing processing time while maintaining analysis comprehensiveness.
Solution Approach 2:
The system segments the extensive threat data into distinct categories, time periods, and threat types for parallel processing. By dividing the large dataset into manageable segments that can be analyzed independently and concurrently, the system reduces overall processing time while maintaining comprehensive coverage of all data sources.
Data Source
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AI summary
A method for quantitative unified analysis of unstructured threat intelligence data, the method to be performed by a computer system comprising: receiving unstructured threat intelligence data from molecular level sources, wherein the unstructured threat intelligence data comprises security events generated from the molecular level sources; uniting the received unstructured threat intelligence data by translating the received threat intelligence data into a uniformed meta-format and storing the translated data in a database, wherein each translated data includes at least a time component; determining isolated data points from the translated data stored in the database by grouping the translated data into groups, wherein each group represents a unique time period, and applying unsupervised machine learning techniques to each group of the translated data; determining data outliers from the isolated data points; and generating a learned function using the data outliers.