Hypergraph Framework for Proactive Malicious Account Detection
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Solution Overview
Problem
Conventional attack detection systems are reactive, often detecting malicious accounts only after they have been used for malicious activities, and rely on manual efforts and labeled data, making them ineffective in preventing large-scale, coordinated attacks and difficult to implement across different online services.
Innovation Solution
A big-data analysis framework using large graph analysis and machine learning techniques to detect malicious accounts and activities without historical data, constructing hypergraphs from user events and generating risk models or classifiers for real-time detection, enabling proactive identification of malicious accounts and activities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional attack detection systems are used, then detection can be performed with simple methods, but detection occurs only after malicious activities have taken place and relies on manual efforts and labeled data
Solution Approach 1:
The system performs preliminary actions by constructing hypergraphs from user event data and using graph analysis to identify suspicious patterns before malicious activities are fully executed. The bootstrapping process pre-processes data to generate initial detection signals that enable early identification of malicious accounts before they are used for attacks.
Solution Approach 2:
The system implements self-service by automatically generating training data through its own graph analysis operations. The detected suspicious accounts and activities from graph analysis are fed back as training data for machine learning classifiers, creating a self-improving system that does not rely on external labeled data or manual efforts.
2Adaptability or versatility
If manual rule creation and labeled training data are used, then detection systems can be implemented, but scalability across different online services becomes difficult
Solution Approach 1:
The system achieves universality by using a unified hypergraph construction approach that can process various types of user event data from different online services. The graph analysis framework and machine learning classifiers are service-agnostic, allowing the same system to detect malicious accounts across diverse platforms without requiring service-specific customization.
Solution Approach 2:
The system eliminates the need for manual rule creation and labeled training data by automatically generating training data through its own operations. The bootstrapped training data is service-specific yet generated automatically, allowing the system to scale to new services without requiring manual adaptation of detection rules or collection of labeled data.
3Productivity
If reactive detection methods are used, then implementation is simpler, but large-scale coordinated attacks cannot be prevented
Solution Approach 1:
The system performs preliminary analysis by constructing hypergraphs that capture relationships among user accounts, devices, and activities before attacks occur. The graph analysis identifies suspicious patterns and clusters of potentially malicious accounts in advance, enabling the system to prevent large-scale coordinated attacks by detecting them at an early stage rather than reacting after damage is done.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for detecting malicious attacks. One of the methods includes generating a collection of hypergraphs representing user events across a collection of users; analyzing the collection of hypergraphs to determine a group of malicious user accounts or account activities satisfying a threshold confidence; using the group of malicious user accounts or account activities as training data for a machine learning system that generates one or more classifiers; and using the one or more generated classifiers to output additional malicious user accounts or account activities.