IDS IP Blacklisting Using Historical Context to Cut False Positives
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Solution Overview
Problem
Existing intrusion detection systems fail to systematically block hostile infrastructure due to reliance on specific patterns, leading to manual review overload and risk of spoofed IP addresses causing network disruptions, while indiscriminate blocking affects regular operations.
Innovation Solution
Automated intrusion alert-based blacklisting system that identifies hostile infrastructure by IP address using historical contextual information and blocking thresholds, distinguishing between malicious and regular traffic.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual review of IDS alerts is performed to determine blocking actions, then accuracy in identifying hostile infrastructure is improved, but productivity and scalability deteriorate due to overwhelming number of alerts
Solution Approach 1:
The system performs automatic IP address blacklisting by having the IDS itself generate blacklisting events when threats are detected. The context information database automatically stores and retrieves IP address history, enabling the system to self-manage blacklisting decisions without human intervention, thus resolving the contradiction between accuracy and scalability
Solution Approach 2:
The context information database pre-stores historical IP address data including connection patterns, geographic location, and threat history before blacklisting decisions are needed. This preliminary preparation of contextual data enables rapid automated decision-making when alerts occur, maintaining high accuracy while improving processing speed and scalability
2Speed
If IP addresses are blocked based on single signature match, then response speed to threats is improved, but reliability deteriorates due to IP spoofing risks
Solution Approach 1:
The system uses feedback from historical context information about IP addresses to verify whether blocked IPs are legitimate. By continuously monitoring and comparing current threat patterns against historical behavior data, the system can distinguish between real threats and spoofed IPs, maintaining fast response while improving reliability through iterative verification
Solution Approach 2:
The context information database pre-establishes baseline profiles for legitimate IP addresses based on their historical communication patterns, geographic location, and connection behavior. When a threat alert occurs, the system compares the suspicious IP against these pre-established profiles to quickly determine if blocking is appropriate, maintaining speed while reducing false positives through preliminary characterization
3Measurement precision
If comprehensive historical context information is collected for all IP addresses, then measurement precision in threat detection is improved, but device complexity and resource requirements worsen
Solution Approach 1:
The system extracts only the most critical contextual attributes for each IP address (connection patterns, geographic location, threat history) and stores them in a simplified database schema. By selecting and storing only essential features rather than complete raw data, the system achieves high detection precision while maintaining manageable database complexity and resource requirements
Solution Approach 2:
The context information database implements different levels of detail for different IP addresses based on their threat profile. High-risk IPs receive more detailed monitoring and contextual storage, while low-risk IPs receive minimal tracking. This localized quality adjustment optimizes resource allocation and reduces overall system complexity while maintaining precision where it matters most
Data Source
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AI summary
Examples provide automated intrusion alert-based blacklisting with minimal false positives that ignores regular business operations, scalable to accommodate the volume of IDS alerts received by high-traffic internet-accessible networked systems. Examples identify and block hostile infrastructure IP addresses during the reconnaissance phase based on IDS alert(s). Each IDS alert is automatically reviewed in historical context and triggers IP blocking as necessary. Some examples maintain TCP/IP handshake records, preventing blocking an IP used to conduct regular business operations on the network that a malicious party has spoofed to avoid identification. Based on the historical context of each IP address within the local network environment, specifically regular business operations traffic versus malicious traffic, the IP address is blocked only if the majority of connections therefrom are malicious. This approach provides substantial cost-savings; frees up resources and personnel otherwise necessary for manual processes; and increases overall network security through automated network defense.