Capital one services, llc (20240265104). REDUCED STORAGE OF SEQUENCE MINING DATA simplified abstract
Contents
REDUCED STORAGE OF SEQUENCE MINING DATA
Organization Name
Inventor(s)
Samuel Sharpe of Cambridge MA (US)
Christopher Bayan Bruss of Washington DC (US)
Maximo Moyer of McLean VA (US)
REDUCED STORAGE OF SEQUENCE MINING DATA - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240265104 titled 'REDUCED STORAGE OF SEQUENCE MINING DATA
The abstract of this patent application describes a method and system for detecting malicious activity by analyzing time-ordered sets of action types and generating datasets based on stored sequences.
- The method involves obtaining a time-ordered set of action types and generating a first dataset to indicate the presence of stored sequences in the set.
- A reduced dataset is then generated based on the first dataset, detecting the presence of specific sequences and determining a score between them using a table.
- Malicious activity is detected using a decision model based on the reduced dataset.
Potential Applications: - Cybersecurity systems - Fraud detection systems - Intrusion detection systems
Problems Solved: - Efficient detection of malicious activity - Improved analysis of time-ordered sets of action types
Benefits: - Enhanced security measures - Early detection of potential threats - Streamlined data analysis processes
Commercial Applications: Title: "Advanced Malicious Activity Detection System" This technology can be used in various industries such as finance, healthcare, and e-commerce to enhance security measures and protect sensitive data from cyber threats. The market implications include increased trust from customers and reduced financial losses due to fraud.
Questions about the technology: 1. How does this method improve upon existing malicious activity detection systems? 2. What are the key factors considered in determining the score between sequences in the reduced dataset?
Frequently Updated Research: Stay updated on the latest advancements in cybersecurity and data analysis techniques to enhance the effectiveness of malicious activity detection systems.
Original Abstract Submitted
a method and related system operations include obtaining a time-ordered set of action types and generating a first dataset by determining, for each respective stored sequence of a plurality of stored sequences, a respective dataset element indicating that the respective stored sequence is present in the time-ordered set of action types. the method may also include generating a reduced dataset based on the first dataset by detecting that the first dataset indicates that a first sequence and a second sequence are present in the time-ordered set of action types, determining a reduced dataset element based on the detection of a presence of the first sequence and the second sequence in the time-ordered set of action types and a score between the first sequence and the second sequence indicated by a table, and detecting malicious activity using a decision model based on the reduced dataset.
