API Context Map Intrusion Detection and Honeypot Redirection
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Solution Overview
Problem
Current cyber intrusion detection systems are inadequate for real-time identification and analysis of intruders, as they often fail to perform comprehensive checks in a timely manner, allowing intruders to bypass initial defenses and requiring extensive human resources for post-incident investigation.
Innovation Solution
A system utilizing an application programming interface (API) layer to redirect suspected intruders to a secure, fake area of the system, allowing real-time detection and analysis while notifying security systems of the intrusion route, and using machine learning to prevent future attacks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If comprehensive checks are performed to identify the intruder and hacking method, then the accuracy of intruder identification is improved, but the time required for detection increases
Solution Approach 1:
The system performs preliminary actions by pre-establishing API context maps that define standard authorized routes and parameters before intrusions occur. When an intrusion is detected, the system compares the intruder's API calls against these pre-established contexts, enabling rapid identification without time-consuming analysis during the actual detection event
Solution Approach 2:
The system creates a copy of the legitimate API context map that mirrors the structure and patterns of authorized API usage. This copied context map serves as a reference framework that enables the system to quickly identify deviations caused by intruders, achieving fast detection without sacrificing identification accuracy
2Reliability
If IP address blocking is implemented to block intruders, then the immediate security threat is reduced, but the intruder can easily bypass by using different IP addresses
Solution Approach 1:
The system introduces an intermediary layer of API context analysis between the intruder and the blocked resources. Instead of directly blocking IP addresses, the system monitors and analyzes API call patterns, comparing them against established context maps to identify and block malicious behavior regardless of the source IP address
Solution Approach 2:
The system changes the blocking parameters from static IP addresses to dynamic API context characteristics. By monitoring parameters such as API endpoints accessed, request patterns, response codes, and sequencing, the system can identify and block intruders based on their behavioral fingerprint rather than their network identity, making bypass significantly more difficult
3Measurement precision
If manual investigation is performed by security personnel to analyze intrusion circumstances, then the depth of security analysis is improved, but the resource consumption and time required increase
Solution Approach 1:
The system performs self-service by automatically generating comprehensive security analysis reports through API context comparison. The system independently identifies intrusion methods, maps attack paths, and produces detailed findings without requiring manual security personnel intervention, thereby maintaining deep analysis capability while eliminating resource consumption associated with manual investigation
Data Source
AI summary
A system, method, and computer program product are provided for real-time cyber intrusion detection and intruder identity analysis. In operation, an intrusion detection system detects an intrusion by a cyber intruder attempting to access a first area of a system utilizing an application programming interface layer (API) layer. The intrusion detection system directs the cyber intruder to a secure second area of the system that mimics the first area of the system via the API layer. Further, the intrusion detection system notifies a security system of the intrusion.