Automated Intrusion Detection Rule Generation
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Solution Overview
Problem
Conventional security platforms face inefficiencies in detecting intrusive activities due to manual and time-consuming detection engineering processes, leading to human errors, strain on resources, and sub-optimal rule management, which decreases threat coverage and increases operational costs.
Innovation Solution
Implementing an automated data-driven detection system that generates and updates intrusion detection rules using machine learning models, applying rule generation policies to data sets, and testing rules for precision, allowing for efficient and continuous threat coverage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual detection engineering is used, then security professionals can create detection rules, but the process is time-consuming and strains human resources
Solution Approach 1:
The system enables self-service detection rule generation by automatically analyzing security data and generating detection rules without requiring manual security professional intervention. The automated rule generation engine processes security events, identifies patterns, and creates detection rules autonomously, freeing security professionals from time-consuming manual rule creation while maintaining rule quality through systematic analysis.
Solution Approach 2:
The patent replaces the mechanical manual process of detection rule creation with an automated computational system. The rule generation engine uses algorithmic processing to analyze security data, identify intrusion patterns, and generate detection rules automatically, substituting human manual labor with machine-based automation that operates continuously without fatigue or time constraints.
2Ease of operation
If manual detection engineering is used, then security professionals can develop detection rules, but human errors occur and effectiveness decreases
Solution Approach 1:
The system performs self-service detection rule generation through automated analysis of security data, eliminating reliance on human professionals who are susceptible to errors. The automated engine systematically processes security events, applies consistent analysis criteria, and generates rules without human intervention, thereby improving reliability while maintaining ease of operation through automated workflows.
Solution Approach 2:
The system incorporates feedback mechanisms where generated detection rules are tested against security data, and performance metrics are fed back into the rule generation process. This continuous feedback loop allows the system to learn from results, refine rule generation algorithms, and improve detection accuracy over time, reducing errors while maintaining operational simplicity.
3Productivity
If more security personnel are deployed to improve threat coverage, then detection capability increases, but operational costs increase
Solution Approach 1:
The automated rule generation engine performs self-service detection engineering, continuously analyzing security data and generating detection rules without requiring additional security personnel. This automation maintains high threat coverage productivity while eliminating the need for increased human resources, thereby reducing operational costs associated with deploying more security staff.
Solution Approach 2:
The system enables continuous detection rule generation and updating without interruption or additional human intervention. The automated engine operates continuously, constantly improving threat coverage through uninterrupted analysis of security data, thereby maintaining high productivity without the escalating operational costs of sustained human effort.
4Adaptability or versatility
If manual rule management is used, then detection rules can be created, but resource strain increases and efficiency decreases
Solution Approach 1:
The system enables self-service detection rule management where the automated engine independently analyzes security data, generates detection rules, and updates existing rules based on new threats. This automation maintains detection rule flexibility and adaptability while dramatically improving management efficiency by eliminating manual rule creation and updates that strain human resources.
Solution Approach 2:
The automated rule generation engine operates continuously, constantly analyzing security data and updating detection rules without interruption. This continuous operation improves productivity by maintaining up-to-date detection capabilities without the periodic manual intervention required in traditional systems, thereby enhancing efficiency while preserving adaptability to new threats.
Data Source
AI summary
A plurality of data sets characterizing prior intrusive activities with respect to computing resources associated with one or more entities are received at a security platform. One or more rule generation policies each pertaining to at least one type of intrusive activity are received at a security platform. The one or more rule generation policies are applied to the plurality of data sets characterizing the prior intrusive activities to generate a plurality of intrusive activity detection rules. The plurality of intrusive activity detection rules are caused to be used to detect subsequent intrusive activities.