18183384. CONTINUOUSLY PROPAGATING KNOWLEDGE TO DECIPHER UNKNOWN MALWARE IN ZERO-TRUST ARCHITECTURES simplified abstract (Dell Products L.P.)
Contents
CONTINUOUSLY PROPAGATING KNOWLEDGE TO DECIPHER UNKNOWN MALWARE IN ZERO-TRUST ARCHITECTURES
Organization Name
Inventor(s)
Isabella Costa Maia of São Paulo (BR)
Karen Stéfany Martins of Belo Horizonte (BR)
Pablo Nascimento Da Silva of Niterói (BR)
Werner Spolidoro Freund of Rio de Janeiro (BR)
CONTINUOUSLY PROPAGATING KNOWLEDGE TO DECIPHER UNKNOWN MALWARE IN ZERO-TRUST ARCHITECTURES - A simplified explanation of the abstract
This abstract first appeared for US patent application 18183384 titled 'CONTINUOUSLY PROPAGATING KNOWLEDGE TO DECIPHER UNKNOWN MALWARE IN ZERO-TRUST ARCHITECTURES
Abstract: One example method includes deploying a malware detection model in a production environment, performing a monitoring process that comprises capturing data from the production environment, by the malware detection model, determining, by the malware detection model, that a likelihood that the data belongs to a domain known to the malware detection model falls below a threshold, determining, by the malware detection model, whether or not the data is noise, or comes from a new domain not known to the malware detection model, and when it is determined that the data comes from the new domain, adapting the malware detection model by incorporating knowledge about the new domain in the malware detection model so that the malware detection model is operable to detect malware in the new domain, as well as the known domain.
- Simplified Explanation:
- A method for deploying a malware detection model in a production environment and adapting it to detect malware in new domains.
- Key Features and Innovation:
- Deployment of malware detection model in production environment. - Monitoring process to capture data. - Determining likelihood of data belonging to known domain. - Adapting model to detect malware in new domains.
- Potential Applications:
- Cybersecurity applications. - Threat detection in various industries. - Data protection in sensitive environments.
- Problems Solved:
- Enhancing malware detection capabilities. - Improving security measures in production environments. - Adapting to evolving threats in cybersecurity.
- Benefits:
- Increased accuracy in malware detection. - Real-time monitoring and response to threats. - Enhanced protection of sensitive data.
- Commercial Applications:
- "Adaptive Malware Detection Model for Production Environments: Enhancing Cybersecurity Measures and Threat Detection"
- Questions about Adaptive Malware Detection Model:
1. How does the model adapt to detect malware in new domains? - The model adapts by incorporating knowledge about the new domain to improve detection capabilities.
2. What are the potential implications of using this technology in various industries? - The technology can enhance cybersecurity measures and threat detection in industries such as finance, healthcare, and government sectors.
Original Abstract Submitted
One example method includes deploying a malware detection model in a production environment, performing a monitoring process that comprises capturing data from the production environment, by the malware detection model, determining, by the malware detection model, that a likelihood that the data belongs to a domain known to the malware detection model falls below a threshold, determining, by the malware detection model, whether or not the data is noise, or comes from a new domain not known to the malware detection model, and when it is determined that the data comes from the new domain, adapting the malware detection model by incorporating knowledge about the new domain in the malware detection model so that the malware detection model is operable to detect malware in the new domain, as well as the known domain.
