Cyber-Semantic Account Management System for Anomaly Detection
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Solution Overview
Problem
Traditional defensive measures are ineffective in identifying malicious activity associated with compromised login credentials or insider attacks, as unauthorized users can gain access using valid credentials, posing a risk to computer systems and networks.
Innovation Solution
A cyber-semantic account management system (C-SAMS) generates behavior profiles based on raw data, such as weblog data, to define patterns of behavior for entities, allowing for the identification of anomalous behavior through statistical analysis and countermeasures like revoking credentials or denying access when deviations from the pattern are detected.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional defensive measures are used to identify malicious activity, then system security is maintained through standard authentication, but unauthorized users with compromised credentials can still access the system undetected
Solution Approach 1:
The system implements continuous feedback loops by monitoring user behavior patterns over time and using this feedback to dynamically adjust security decisions. Behavior profiles are continuously updated with new data, and real-time comparisons detect deviations that trigger security responses, creating a self-improving detection system
Solution Approach 2:
The patent introduces behavior profiles as an intermediary layer between authentication and system access. Instead of relying solely on credential validation, the system uses behavior patterns as a mediator to evaluate whether authenticated users should be granted access, adding a detection layer that works alongside traditional security measures
2Measurement precision
If behavior profiles are generated and continuously monitored to detect anomalous behavior, then detection precision is improved, but system complexity increases
Solution Approach 1:
The system creates simplified copies of user behavior patterns in the form of behavior profiles that capture essential characteristics without replicating the full complexity of actual user interactions. These profile copies enable efficient comparison and anomaly detection while maintaining manageable system complexity
Solution Approach 2:
The monitoring system is segmented into distinct functional modules: data collection components that gather raw behavior data, profile generation components that create behavior patterns, comparison components that detect deviations, and response components that execute security actions. This segmentation allows each module to be optimized independently
3Measurement precision
If comprehensive raw data is collected and analyzed to create behavior profiles, then detection accuracy is improved, but data processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by pre-processing and storing raw behavior data in structured formats, and by pre-establishing behavior profiles during periods when anomaly detection is less critical. This preparation work reduces the computational burden during real-time monitoring and detection phases
Solution Approach 2:
The system applies partial action by focusing analysis on the most significant behavior patterns and deviations rather than processing every detail equally. Statistical analysis methods identify and prioritize the most relevant deviations from behavior profiles, reducing unnecessary computational effort on minor variations
Data Source
AI summary
Systems, methods, and apparatus for identifying anomalous behavior are provided. For example, a method may include receiving raw data, generating a behavior profile for the entity based on the raw data, receiving comparison data, determining whether the comparison data deviates from a pattern of behavior defined in the behavior profile, and identifying the comparison data as anomalous behavior when the comparison data deviates from the pattern of behavior. In one embodiment, the raw data includes recorded activity for the entity. In one embodiment, the behavior profile defines a pattern of behavior for the entity. In one embodiment, a countermeasure is performed upon identifying anomalous behavior. The countermeasure may include at least one of revoking the entity's credentials, denying the entity access to a resource, shutting down access to a port, and denying access to the entity. The method may further include providing a report of the anomalous behavior.