Edge Reconnaissance Detection for Datacenter Request Blocking
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Solution Overview
Problem
Current approaches fail to detect malicious reconnaissance calls and activities, and there are no mechanisms in place to learn the circumstances to identify and counter reconnaissance processes within datacenters, leaving critical data and services vulnerable to cyber attacks.
Innovation Solution
Implementing an edge monitoring module in datacenters that uses machine learning to analyze incoming requests and processes, identifying deviations from normal patterns, and blocking responses to suspected reconnaissance activities, while leveraging edge computing configurations and CDN aggregators for distributed security monitoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional security monitoring approaches are used, then system performance is maintained, but reconnaissance activities cannot be detected
Solution Approach 1:
The patent introduces an edge monitoring module as an intermediary component between network requests and the core datacenter system. This module intercepts and analyzes requests before they reach the main system, enabling detection of reconnaissance activities without requiring the core system to perform complex analysis functions. The intermediary handles the detection complexity while preserving the simplicity and performance of the main system.
Solution Approach 2:
The monitoring function is segmented into a separate edge module rather than being integrated into the core datacenter infrastructure. This segmentation allows the detection system to operate independently with specialized algorithms while the main system continues to handle data processing tasks. The segmentation enables parallel operation of detection and data processing functions.
2Measurement precision
If comprehensive analysis of all requests is performed, then detection accuracy improves, but system performance deteriorates
Solution Approach 1:
The edge monitoring module applies localized analysis only to specific aspects of requests that are indicative of reconnaissance activities, rather than performing comprehensive analysis of all request data. The module focuses on detecting patterns such as unusual request frequencies, targeted probing behaviors, and suspicious query types, while allowing normal requests to pass through with minimal processing. This selective local analysis maintains detection accuracy for malicious activities while preserving overall system throughput.
3Object-affected harmful factors
If response generation is blocked for suspected reconnaissance, then security improves, but legitimate requests may be affected
Solution Approach 1:
The system performs preliminary analysis and classification of requests at the edge monitoring module before they reach the core system. By identifying and flagging reconnaissance activities in advance, the system can prevent harmful requests from consuming core system resources. The preliminary action includes establishing baseline behaviors and detecting deviations that indicate malicious intent, allowing the system to take preventive measures before actual attacks occur.
Solution Approach 2:
The monitoring system incorporates feedback mechanisms that continuously learn from analyzed requests and adjust detection thresholds. The system monitors the effects of blocking decisions and refines its detection algorithms to reduce false positives. Feedback loops allow the system to adapt to new attack patterns while maintaining operational smoothness for legitimate requests, balancing security enforcement with system usability.
Data Source
AI summary
A method comprises receiving one or more requests for data, wherein the one or more requests are received over at least one computer network, analyzing at least one of the one or more requests and one or more processes performed in response to the one or more requests to determine whether the one or more requests comprise reconnaissance for a cyber attack, and preventing at least one of generation and transmission of one or more responses to the one or more requests in response to determining that the one or more requests comprise the reconnaissance for the cyber attack.