Cognitive Fingerprinting for Behavioral Anomaly Detection
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Solution Overview
Problem
Current security measures, such as anti-virus products and multi-factor authentication, are inadequate in detecting unauthorized behavior by authorized users, particularly in preventing subtle data theft over time, and existing learning approaches are inflexible and require significant domain-specific information.
Innovation Solution
A system and method for creating a core cognitive fingerprint that analyzes data over time to detect anomalies and unauthorized behavior by using pattern recognizers with assigned heuristics, extracting relationships, and comparing results against learned weights to generate a final cognitive fingerprint for identifying potential security breaches.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If pre-computed signatures are used to identify viruses and threats, then detection accuracy is improved, but response time deteriorates because there is not always enough time for a threat to be found, analyzed, and an update delivered
Solution Approach 1:
The system performs preliminary analysis by creating cognitive fingerprints of normal system behavior in advance. When a threat is detected, the system compares it against these pre-established behavioral patterns rather than waiting for signature updates. This allows the system to identify anomalies based on deviations from normal behavior patterns that were captured during baseline periods.
Solution Approach 2:
The patent replaces the traditional mechanical signature-matching system with a behavioral analysis system. Instead of relying on pre-computed signatures that require updates, the system uses pattern recognizers and cognitive fingerprints to detect threats based on behavioral anomalies, enabling faster response to novel threats without waiting for signature databases to be updated.
2Reliability
If multi-factor authentication schemes such as retina and fingerprint scans are implemented, then identity validation is improved, but cost increases and they can be defeated when an authorized user engages in unauthorized removal or theft of information
Solution Approach 1:
The system introduces cognitive fingerprint analysis as an intermediary layer between authentication and data access. Even when multi-factor authentication successfully validates identity, the system continuously monitors user behavior and compares it against the cognitive fingerprint. This intermediary behavioral analysis layer provides an additional safeguard that detects when authorized users engage in unauthorized activities without requiring changes to the authentication mechanism itself.
Solution Approach 2:
The system implements continuous feedback monitoring of user behavior patterns. By comparing actual user actions against the cognitive fingerprint established during baseline periods, the system provides real-time feedback on whether behavior is consistent with authorized usage patterns, enabling detection of unauthorized activities even after successful authentication.
3Adaptability or versatility
If neural network based recognition systems process inputs as sequences of bits, then content agnosticism is improved, but learning speed deteriorates and deep structures are required to capture enough pattern information
Solution Approach 1:
The patent segments the pattern recognition process into specialized components, each handling specific types of data patterns. Instead of using a single deep neural network that must learn all patterns from scratch, the system divides recognition tasks among multiple pattern recognizers that can be tailored to specific content types, reducing the learning burden and improving efficiency while maintaining content agnosticism at the system level.
Solution Approach 2:
The system applies local quality by assigning different processing characteristics to different parts of the recognition system. Each pattern recognizer is optimized for specific types of patterns it needs to detect, rather than using a uniform deep learning approach for all inputs. This allows faster, more efficient pattern matching while maintaining the ability to handle diverse content types through the overall system architecture.
4Extent of automation
If inductive logic programming methods process higher-level concepts, then reasoning capability is improved, but flexibility deteriorates because they require a lot of domain specific information, customization and may not scale to handle variations of problems from adjacent domains
Solution Approach 1:
The system implements universality by creating a platform that can handle multiple domains through a common cognitive fingerprinting framework. The pattern recognizers and behavioral analysis mechanisms are designed to be domain-agnostic, allowing the same system to detect threats across different domains (financial, healthcare, government) without requiring extensive domain-specific customization. The system adapts to different domains by learning domain-specific normal behavior patterns during baseline periods rather than requiring hard-coded domain knowledge.
Data Source
AI summary
A system and method for creating a core cognitive fingerprint are provided. A core cognitive fingerprint can be used to capture and summarize the evolution of a system state and potentially respond with a predetermined action if the fingerprint falls within a defined threshold. The method includes: identifying a set of time frames, each time frame corresponding to a respective data source, within which data is extracted; providing a plurality of pattern recognizers each having an assigned heuristic specific to a type of content; processing the extracted data through the plurality of pattern recognizers to generate an initial set of elements, each element corresponding to the output of the heuristic assigned to each pattern recognizer; extracting identified relationships amongst the initial set of elements; modifying the initial set of elements to include the identified relationships to create an intermediate set of elements; comparing the intermediate set of elements against assigned values to emphasize or deemphasize each element in the intermediate set of elements to create a final set of elements; and using the final set of elements as a cognitive fingerprint representing a signature of the data extracted from the time frame, so that the signature can be compared to other cognitive fingerprints for further analysis.