Adaptive Web Vulnerability Scanner Detection System
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Solution Overview
Problem
Publicly-facing web services are vulnerable to vulnerability scanning, which can lead to malicious activities such as denial of service, data corruption, and data exfiltration, and existing methods to detect these scans require substantial manual effort and are brittle, failing to account for new or modified scanner variations.
Innovation Solution
A system that uses a dictionary of patterns from known vulnerability scanners to generate feature vectors and clusters, allowing for real-time identification and alerting of vulnerability scanning activity, and adaptive updating of signatures to detect evolving scanning behavior.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual configuration of vulnerability scanner signatures is used, then detection accuracy for known scanners is improved, but operational complexity and maintenance burden increase substantially
Solution Approach 1:
The system performs self-learning by automatically analyzing web request patterns to identify vulnerability scanner behavior. It builds and updates its own detection models without requiring manual configuration, allowing the system to serve itself in detecting new scanner variations while maintaining detection accuracy.
Solution Approach 2:
The system continuously monitors web traffic patterns and uses this feedback to refine its detection models. By analyzing actual scanner behavior patterns and comparing them against learned signatures, the system automatically updates its detection capabilities to account for new scanner variations.
2Reliability
If static vulnerability scanner signatures are used, then detection of known scanners is achieved, but adaptability to new or modified scanner variations is lost
Solution Approach 1:
The system transitions from static signatures to dynamic detection models that continuously evolve. It learns from actual scanner behavior patterns in real-time, allowing the detection mechanisms to adapt to new scanner variations while maintaining reliable detection of known scanners through ongoing pattern analysis.
Solution Approach 2:
The system maintains continuous learning and adaptation by constantly analyzing web request patterns. This uninterrupted process ensures that detection capabilities are continuously updated to reflect the latest scanner techniques while maintaining consistent detection performance.
3Measurement precision
If comprehensive monitoring of all web requests is implemented, then detection capability is improved, but processing time and computational resources increase
Solution Approach 1:
The system extracts and focuses on specific critical patterns from the vast amount of web request data. By identifying and monitoring only the most indicative patterns of vulnerability scanner behavior rather than processing every request in detail, it maintains high detection capability while reducing processing time.
Solution Approach 2:
The system applies partial monitoring by analyzing only the most relevant request characteristics and patterns. It selectively processes data that indicates potential vulnerability scanning behavior, avoiding unnecessary processing of benign traffic while maintaining effective detection.
Data Source
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AI summary
A method includes obtaining a dictionary, data for a set of web requests, and definitions of a first set of clusters associated with vulnerability scanners. The method includes identifying a set of clients that transmitted the second set of web requests. The method includes generating a second set of feature vectors, which each corresponds to one of the clients. Each element in each feature vector corresponds respectively to an entry in the dictionary. The method includes clustering the second set of feature vectors into a second set of clusters. The method includes, in response to a first distance between a selected cluster of the second set of clusters and one of the first set of clusters being less than a first predetermined distance, (i) identifying one of the set of web services that received web requests corresponding to feature vectors in the selected cluster and (ii) generating a scanning alert.