Collaborative Filtering for Cybersecurity Anomaly Detection
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Solution Overview
Problem
Current cybersecurity solutions face challenges in detecting unauthorized access to cloud storage resources, particularly in recognizing suspicious activity by legitimate users and identifying zero-day attacks or insider threats, as they often rely on rigid rules or supervised machine learning models that require labeled data and may generate false alarms.
Innovation Solution
Adaptation of collaborative filtering techniques for intrusion detection, using actor IDs and resource IDs to compute a recommendation score that indicates risk, allowing for the detection of unauthorized access without labeled data and improving the efficiency of anomaly detection in cybersecurity systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If supervised machine learning models are used for intrusion detection, then detection accuracy can be improved, but the requirement for labeled data and system complexity increases
Solution Approach 1:
The patent replaces traditional supervised machine learning models with collaborative filtering algorithms adapted from recommendation systems. This substitution eliminates the need for complex labeled training data while maintaining detection effectiveness, as the collaborative filtering approach leverages patterns from multiple users' access behaviors rather than requiring pre-labeled attack data.
Solution Approach 2:
The patent applies collaborative filtering technology originally designed for recommendation systems to the cybersecurity domain. This universal application allows the system to detect intrusions by adapting patterns from legitimate user behavior, making the solution more versatile and reducing dependency on domain-specific labeled data.
2Device complexity
If rigid rules are used for security detection, then system complexity is reduced, but false alarms increase and detection accuracy decreases
Solution Approach 1:
The patent replaces rigid rule-based detection systems with collaborative filtering algorithms that dynamically analyze user behavior patterns. This substitution reduces false alarms by understanding the context and patterns of legitimate user actions, while maintaining relatively simple system architecture compared to supervised machine learning approaches.
3Reliability
If supervised machine learning models are deployed, then ability to detect known attacks improves, but false alarms increase and efficiency decreases
Solution Approach 1:
The patent adapts collaborative filtering from recommendation systems to detect both legitimate user behavior and potential intrusions. This universal approach improves detection efficiency by leveraging patterns from multiple users simultaneously, reducing false alarms, and maintaining high reliability without requiring complex supervised learning pipelines.
4Productivity
If collaborative filtering is adapted for intrusion detection, then false alarms are reduced and detection efficiency improves, but the system requires adaptation of existing code libraries
Solution Approach 1:
The patent leverages existing collaborative filtering code libraries originally designed for recommendation systems and adapts them for cybersecurity applications. This approach improves detection efficiency by using proven algorithms while reducing implementation complexity through reuse of established code, requiring only domain-specific adaptation rather than building from scratch.
Data Source
AI summary
A machine learning model is trained using tuples that identify an actor, a resource, and a rating based on a normalized count of the actor's attempts to access the resource. Actors may be users, groups, IP addresses, or otherwise defined. Resources may be storage, virtual machines, APIs, or otherwise defined. A risk assessor code feeds an actor-resource pair to the trained model, which computes a recommendation score using collaborative filtering. The risk assessor inverts the recommendation score to obtain a risk measurement; a low recommendation score corresponds to a high risk, and vice versa. The risk assessor code or other code takes cybersecurity action based on the recommendation score. Code may accept a risk R, or aid mitigation of the risk R, where R denotes a risk that the scored pair represents an unauthorized attempt by the pair actor to access the pair resource.