Request Sequence Modeling for Coordinated Attack Detection
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Solution Overview
Problem
Conventional techniques struggle to detect coordinated malicious activities on online platforms, requiring extensive human effort and are ineffective against adaptive malicious users who change identifiers, making it difficult to scale security measures.
Innovation Solution
The system employs a term frequency-inverse document frequency (TF-IDF) approach to model sequences of requests, automatically identifying coordinated malicious activities by encoding request paths into standardized tokens and using frequency embeddings to compare sequences against validated attack patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional techniques are used to detect malicious activities, then detection can be performed with simple methods, but detection accuracy is low and extensive human effort is required
Solution Approach 1:
The patent introduces frequency embeddings as an intermediary representation that bridges raw request sequences and detection algorithms. These embeddings capture the temporal patterns and characteristics of request sequences, enabling automated detection of coordinated malicious activities without requiring complex manual analysis while significantly improving detection accuracy
Solution Approach 2:
The patent replaces manual human analysis with automated machine learning-based detection systems. By using frequency embeddings and automated pattern recognition, the system eliminates the need for extensive human effort in detecting malicious activities, while maintaining or improving detection accuracy through consistent algorithmic application
2Adaptability or versatility
If traditional security measures are applied, then implementation is straightforward, but they are ineffective against adaptive malicious users who change identifiers
Solution Approach 1:
The patent implements dynamic detection by analyzing sequences of requests over time rather than static individual requests. The frequency embedding approach captures temporal patterns and evolves with changing attack strategies, automatically adapting to new malicious behaviors without requiring manual reconfiguration, thus maintaining operational simplicity while improving adaptability
Solution Approach 2:
The system performs preliminary analysis by creating frequency embeddings from request sequences before detection occurs. This pre-processing captures the essential characteristics of normal and malicious patterns, enabling the system to quickly adapt to new threats by comparing against established patterns without complex real-time adjustments
3Productivity
If manual detection methods are used, then false positives can be reviewed, but productivity is low and scaling is difficult
Solution Approach 1:
The detection system performs self-service by automatically analyzing request sequences, generating frequency embeddings, and identifying malicious patterns without human intervention. This automation dramatically increases detection throughput while maintaining reliability through consistent algorithmic application and the ability to process large volumes of data systematically
Solution Approach 2:
The system incorporates feedback mechanisms where detection results and pattern recognition improve over time. By continuously analyzing request sequences and updating frequency embeddings, the system learns from past detections and improves reliability while maintaining high productivity through automated processing
4Productivity
If automated detection systems are implemented, then productivity increases, but system complexity increases
Solution Approach 1:
The patent segments the detection process into distinct components: request sequence collection, frequency embedding generation, pattern matching, and detection decision-making. This segmentation allows each component to be optimized independently and processed in parallel, increasing productivity while managing complexity through modular architecture that can be implemented incrementally
Data Source
AI summary
Embodiments of the disclosed technologies create a term frequency-inverse document frequency (tf-idf) model of interactions of user accounts with an online system, and, using the tf-idf model, identify a subset of the user accounts as being involved in a malicious use of the online system. The tf-idf model is created by, for a user account, storing a sequence of requests received by the online system from the user account over a time interval as a document, where a request includes a digital communication from the user account to the online system, and generating a feature embedding for the sequence of requests, where the feature embedding is based on a relationship between a frequency of occurrence of a request in the document and a number of documents that include the request.