AI Pattern Analysis Engine for Network Threat Detection
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Solution Overview
Problem
Current malware and virus prevention applications struggle to differentiate between legitimate and illegitimate software activity, allowing threat actors to bypass security measures by coding checks for publicly identifiable information, and fail to customize security based on unique network characteristics, leading to ineffective threat mitigation.
Innovation Solution
Implementing a network system with centralized and local AI-based pattern analysis engines that identify normal activity patterns on each computing device and across the network, using machine learning to filter out normal activity and alert on anomalous behavior, and continually update baseline patterns to enhance security.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If standard malware prevention applications are used to scan and block malicious code, then basic threat protection is provided, but threat actors can easily bypass these measures by coding checks for publicly identifiable information and detecting sandboxed environments
Solution Approach 1:
The system implements customized pattern analysis for each enterprise network by learning normal operational patterns specific to that network's computing devices, applications, and user behaviors. This local customization allows the security system to adapt to unique network characteristics rather than applying generic detection rules, making it resistant to bypass attempts using publicly identifiable information.
Solution Approach 2:
The system performs preliminary learning of normal operational patterns during a baseline period before deployment of the security detection. This preliminary action establishes a customized understanding of legitimate network behavior specific to each enterprise, enabling the system to later identify anomalies that deviate from the established baseline rather than relying on pre-existing generic threat signatures.
2Measurement precision
If comprehensive pattern analysis is performed on all computing operations to identify malicious activity, then threat detection accuracy is improved, but system complexity and computational resources increase significantly
Solution Approach 1:
The system extracts and focuses analysis only on patterns that deviate from the learned baseline of normal operational behavior. By taking out the common normal patterns and excluding them from continuous analysis, the system achieves high threat detection accuracy while reducing computational complexity, as it only needs to flag and deeply analyze anomalous deviations rather than continuously examining all operations.
Solution Approach 2:
The system automatically learns and updates the baseline of normal operational patterns for each network without requiring manual configuration or intervention. This self-service capability reduces system complexity by eliminating the need for manual pattern definition and maintenance, while still achieving high detection accuracy through automated adaptation to each network's unique behavior.
3Reliability
If AI models are continually trained to update baseline common activity patterns, then the system adapts to changing normal behavior and reduces false alerts, but training time and computational resources increase
Solution Approach 1:
The system performs AI model training periodically rather than continuously, updating baseline patterns at scheduled intervals or when significant changes in network behavior are detected. This periodic training approach reduces computational overhead and time loss compared to continuous training, while still maintaining reliability by regularly adapting to changing normal behaviors and reducing false alerts through updated baselines.
Data Source
AI summary
A network system of pattern analysis includes a centralized AI-based pattern analysis engine and each computing device comprises a local AI-based pattern analysis engine. The pattern analysis engine(s) each analyze computing operations on a local machine basis or a on a network basis depending on where installed. The AI-based pattern analysis engines identify common activity patterns for each machine and exclude the common activity patterns from further analysis of the computing operations, leading to more efficient identification of activity patterns indicative of nefarious activity. Once detected, the AI-based pattern analysis engines trigger an incident response to counter the nefarious activities. The AI-based pattern analysis engines include AI models that are continually or periodically trained to update the baseline common activity patterns.