Threat Intelligence Rule Prioritization for Evolving Attack Detection
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Solution Overview
Problem
Existing network security systems struggle to adapt to ever-changing security threats and threat entities due to outdated or insufficient detection rules, lacking comprehensive testing mechanisms, and inefficiencies in identifying and addressing vulnerabilities.
Innovation Solution
A threat intelligence platform that automatically assesses the sufficiency and performance of detection rules, aggregates disparate threat information, and generates prioritization schemes for rule improvements, utilizing AI and machine learning to identify gaps and evolve responses to emerging threats.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If detection rules are manually created and deployed in security systems, then the system can identify and respond to known threats, but the system becomes outdated and ineffective against evolving security threats over time
Solution Approach 1:
The system automatically evaluates detection rules against simulated attack techniques and generates improvement recommendations without human intervention. The platform self-updates by continuously testing rules against evolving threat intelligence, allowing the security system to maintain effectiveness without manual rule creation and deployment cycles
Solution Approach 2:
The system implements continuous feedback loops where detection rules are automatically tested against simulated attacks, performance is measured, and results are used to generate improvement recommendations. This closed-loop feedback mechanism ensures rules remain effective against evolving threats by continuously learning from test outcomes and threat intelligence
2Reliability
If comprehensive attack scenarios are manually built for testing security capabilities, then thorough security validation can be achieved, but significant amounts of effort and resources are required
Solution Approach 1:
The system uses synthetic data that replicates real attack patterns and threat behaviors without requiring actual malicious samples. By copying the essential characteristics of attacks through synthesized representations, the system achieves comprehensive security validation while eliminating the resource-intensive process of manually building and maintaining extensive attack scenario databases
Solution Approach 2:
The platform performs multiple testing functions simultaneously using a unified synthetic data generation engine that can create diverse attack scenarios across different threat types, vectors, and techniques. This multi-functional approach consolidates what would otherwise require separate manual testing efforts into a single automated system
3Adaptability or versatility
If detection rules are continuously updated to address new threats, then the system can maintain current threat detection capabilities, but the complexity of managing and testing rules increases
Solution Approach 1:
The system automatically manages the complexity of detection rule updates by self-evaluating rule performance, identifying gaps through automated testing, and generating improvement recommendations. This self-service approach to rule management eliminates the manual overhead of tracking, testing, and updating numerous detection rules as threats evolve
Solution Approach 2:
The system performs preliminary evaluation and testing of detection rules before deployment using synthetic attack data. By pre-validating rules against simulated threats and identifying potential issues beforehand, the system reduces the complexity of managing updates by catching problems early in the development cycle rather than during live operations
4Loss of information
If threat intelligence is continuously collected and updated, then the system can maintain accurate threat information, but gaps in intelligence related to attack domains may persist
Solution Approach 1:
The system implements automated feedback mechanisms that continuously evaluate threat intelligence completeness by testing detection rules against simulated attacks across all attack domains. Gaps are identified through systematic testing failures, and the feedback loop drives targeted intelligence collection and rule improvements, accelerating the identification and remediation of intelligence gaps
Solution Approach 2:
The system uses synthetic representations of complete attack scenarios to benchmark against actual threat intelligence. By copying ideal comprehensive attack patterns and comparing them against detected threats, the system can quickly identify intelligence gaps without manually reviewing and validating extensive threat data across all domains
Data Source
AI summary
Disclosed are systems and methods for identifying threat events in an enterprise network and managing detection rules and responses to the events. A threat intelligence computer system can receive information about a detected threat event including a phase of attack and a detected domain of the threat event, apply at least one tag to the detected event that associates the event with at least one of the rules triggered in response to detecting the event, evaluate the tagged rules against the information, flag the event as having an improvement opportunity, determine whether the rule tagged to the event is a candidate for improvement, generate, based on the determination, instructions for improving the rule, generate a prioritization scheme indicating an order to address the instructions to improve the rule amongst instructions to improve various threat detection rules, and generate and return output indicating the prioritization scheme for presentation at user devices.