Context-Aware Cyber Threat Detection for Adaptive Traffic Blocking
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Solution Overview
Problem
Existing cyber threat detection systems face challenges in accurately and efficiently responding to dynamic and evolving threats due to the variability and timeliness of cyber threat intelligence data, leading to either allowing malicious traffic or blocking legitimate traffic.
Innovation Solution
The system employs machine-learning models to process and analyze cyber threat intelligence data, determining dispositions on an endpoint-by-endpoint basis, considering threat context and changes, and assessing the impact of blocking legitimate traffic to improve response accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the system waits for additional CTI data from providers before blocking traffic, then the accuracy of threat detection is improved, but the response time to emerging threats increases
Solution Approach 1:
The system performs preliminary analysis of CTI data indicators (such as malware hashes, IP addresses, domains) against known threat patterns before final blocking decisions are made. This preliminary action allows the system to quickly identify obviously malicious traffic while reserving the option to wait for additional data on borderline cases, thus resolving the contradiction between speed and accuracy.
Solution Approach 2:
The system dynamically adjusts its decision-making process based on the confidence level of threat detection. For high-confidence threats (clear malicious indicators), the system blocks immediately without waiting for additional data. For low-confidence threats (ambiguous indicators), the system waits for more CTI data before blocking. This dynamic approach optimizes both response time and detection accuracy.
2Speed
If the system blocks traffic quickly upon receiving CTI data, then the response speed to threats is improved, but legitimate network traffic may be blocked
Solution Approach 1:
The system introduces an intermediary verification step using multiple CTI providers and cross-referencing mechanisms before final blocking decisions. When traffic is flagged as potentially malicious, the system checks multiple independent CTI sources to verify the threat before blocking, reducing false positives while maintaining fast response times through automated parallel verification.
Solution Approach 2:
The system implements feedback loops where blocking decisions are continuously evaluated based on subsequent CTI data and system performance metrics. If legitimate traffic is incorrectly blocked, the system receives feedback and adjusts its decision thresholds or adds exceptions. This feedback mechanism ensures high-speed blocking while protecting legitimate traffic through continuous learning and adaptation.
3Object-affected harmful factors
If the system blocks all traffic from endpoints identified as threats, then the security coverage is improved, but the impact on legitimate business operations increases
Solution Approach 1:
The system applies different blocking strategies to different endpoints and traffic types based on local characteristics. Instead of uniform blocking, the system analyzes each endpoint's traffic patterns, business criticality, and threat level to determine appropriate actions. High-risk endpoints with no legitimate business value receive strict blocking, while endpoints with mixed legitimate and malicious traffic receive selective blocking or monitoring, thus maintaining security coverage while preserving legitimate operations.
Solution Approach 2:
The system segments traffic into different categories (malicious, suspicious, legitimate) and applies different dispositions to each segment. Rather than blocking all traffic from a flagged endpoint, the system segments the traffic flow and blocks only the malicious portions while allowing legitimate business traffic to pass through. This segmentation approach maintains comprehensive security coverage while minimizing impact on legitimate operations.
Data Source
AI summary
Aspects described herein may relate to cyber threat detection based on threat context and/or threat changes. Cyber threat intelligence (CTI) data may be received from a CTI provider. Endpoint data that indicates evidence that endpoints are cyber threats may be determined based on the CTI data. The endpoint data may be analyzed and/or compared to stored data associated with the endpoint. The analysis and/or comparison may be performed to determine whether evidence that the endpoint is a cyber threat has changed. Based on any changes, dispositions for the endpoint may be determined and sent. The dispositions may change how devices filter network traffic associated with the endpoint. Alternatives to default dispositions may be determined based on a impact of blocking potentially legitimate network traffic to and/or from the endpoints. Machine-learning models may assist in processing and analyzing CTI data, performing threat monitoring, and/or determining feeds that include the dispositions.