Protocol-Agnostic Cloud Attack Detection System
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Solution Overview
Problem
Current methods for detecting brute force attacks in cloud-based networks are protocol-specific and not cost-effective, failing to efficiently identify non-targeted attacks across different protocols, which are often shallow and affect multiple machines simultaneously.
Innovation Solution
A generic attack detection system using supervised learning to identify malicious IP addresses performing exploration and exploitation activities, distinguishing between targeted and non-targeted attacks by analyzing interaction patterns and features across various protocols, without being specific to any particular protocol or port.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If protocol-specific detection methods are used, then detection accuracy for specific protocols is improved, but device complexity and cost increase when covering multiple protocols
Solution Approach 1:
The patent implements a universal detection system that can identify non-targeted attacks across multiple protocols (RDP, SSH, FTP, Telnet, etc.) using a single platform. The system extracts protocol-agnostic features from network traffic and uses machine learning models trained on multi-protocol data, eliminating the need for separate detection systems for each protocol while maintaining high detection accuracy.
Solution Approach 2:
The system creates abstracted representations (features) of attack patterns that are protocol-independent. Instead of analyzing raw protocol-specific traffic, the system extracts key characteristics such as connection frequency, packet timing, and interaction patterns that replicate the essential attack signature across different protocols, enabling unified detection without protocol-specific complexity.
2Adaptability or versatility
If multiple protocol-specific detection systems are deployed, then coverage of different protocols is improved, but cost effectiveness deteriorates
Solution Approach 1:
The system provides comprehensive multi-protocol coverage through a single unified platform that can detect attacks on RDP, SSH, FTP, Telnet, and other protocols simultaneously. This eliminates the need to deploy and maintain multiple separate detection systems, significantly reducing licensing, hardware, and operational costs while maintaining broad protocol coverage.
Solution Approach 2:
The patent merges detection capabilities for multiple protocols into a single integrated system. By combining feature extraction, analysis, and detection functions into one unified platform, the system achieves economies of scale, reducing overall costs compared to deploying separate specialized systems for each protocol.
3Reliability
If traditional brute force detection methods are used, then detection capability for targeted attacks is improved, but detection effectiveness for non-targeted attacks deteriorates
Solution Approach 1:
The system dynamically adapts its detection approach based on the attack pattern being detected. For non-targeted attacks, it monitors exploration phase characteristics such as scanning behavior and connection attempts across multiple ports. For targeted attacks, it focuses on exploitation phase patterns. This dynamic adjustment of detection focus based on attack stage and type significantly improves detection effectiveness across different attack scenarios.
Solution Approach 2:
The patent segments the attack detection process into distinct phases: exploration (scanning, port probing) and exploitation (brute force, credential testing). By separating detection strategies for each phase and identifying transitions between them, the system can apply appropriate detection methods for non-targeted versus targeted attacks, improving overall detection precision.
Data Source
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AI summary
A system for detecting a non-targeted attack by a first machine on a second machine is provided. The system includes an application that includes instructions configured to: extract network data corresponding to traffic flow between the first and second machines, where the second machine is implemented in a cloud-based network; identify a first suspect external IP address based on the network data; calculate features for the first suspect external IP address, where the features include exploration type features and exploitation type features; train a classifier based on predetermined examples and the features to generate and update a model; classify the first suspect external IP address based on the model and at least some of the features; and perform a countermeasure if a classification provided from classifying the first suspect external IP address indicates that the first suspect external IP address is associated with a malicious attack on the second machine.