Dell products l.p. (20240311480). CONTINUOUSLY PROPAGATING KNOWLEDGE TO DECIPHER UNKNOWN MALWARE IN ZERO-TRUST ARCHITECTURES simplified abstract
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 20240311480 titled 'CONTINUOUSLY PROPAGATING KNOWLEDGE TO DECIPHER UNKNOWN MALWARE IN ZERO-TRUST ARCHITECTURES
- Simplified Explanation:**
The patent application describes a method for deploying a malware detection model in a production environment, monitoring data captured by the model, and adapting the model to detect malware in new domains.
- Key Features and Innovation:**
- Deploying a malware detection model in a production environment
- Monitoring data to determine likelihood of belonging to a known domain
- Adapting the model to detect malware in new domains
- Potential Applications:**
This technology can be applied in cybersecurity systems, network security, and threat detection software.
- Problems Solved:**
The technology addresses the challenge of detecting malware in new domains that are not known to the detection model, improving overall cybersecurity measures.
- Benefits:**
- Enhanced malware detection capabilities
- Improved protection against evolving cyber threats
- Adaptability to new domains for comprehensive security coverage
- Commercial Applications:**
The technology can be utilized by cybersecurity companies, IT departments, and organizations looking to enhance their threat detection capabilities in dynamic environments.
- Prior Art:**
Researchers can explore existing literature on malware detection models, domain adaptation techniques, and cybersecurity innovations to understand the background of this technology.
- Frequently Updated Research:**
Stay informed on the latest advancements in malware detection, domain adaptation, and cybersecurity to enhance the effectiveness of this technology.
- Questions about Malware Detection Model:**
1. How does the malware detection model adapt to new domains? 2. What are the potential limitations of this technology in detecting advanced malware threats?
1. **A relevant generic question not answered by the article, with a detailed answer:** How does the malware detection model handle false positives in its detection process? False positives can be addressed by implementing additional validation steps in the monitoring process to reduce the chances of misidentifying legitimate data as malware.
2. **Another relevant generic question, with a detailed answer:** What are the key factors to consider when deploying a malware detection model in a production environment? Factors to consider include data privacy regulations, scalability of the model, integration with existing security systems, and ongoing monitoring and updates to ensure optimal performance.
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.
