Rapid7, Inc. (20240333737). TECHNIQUES OF MONITORING NETWORK TRAFFIC IN A CLOUD COMPUTING ENVIRONMENT simplified abstract
Contents
TECHNIQUES OF MONITORING NETWORK TRAFFIC IN A CLOUD COMPUTING ENVIRONMENT
Organization Name
Inventor(s)
Pojan Shahrivar of Stockholm (SE)
TECHNIQUES OF MONITORING NETWORK TRAFFIC IN A CLOUD COMPUTING ENVIRONMENT - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240333737 titled 'TECHNIQUES OF MONITORING NETWORK TRAFFIC IN A CLOUD COMPUTING ENVIRONMENT
Simplified Explanation: This patent application discusses using machine learning techniques to update the configuration of a computer network security system in a cloud computing environment. The process involves analyzing datasets of events detected by the security system, generating signatures for these events using trained ML models, clustering the signatures to identify event clusters, and updating the security system based on the characteristics of these events.
Key Features and Innovation:
- Machine learning techniques are used to update the configuration of a computer network security system in a cloud computing environment.
- Datasets containing information about detected events are analyzed to generate signatures using trained ML models.
- Signatures are clustered to identify event clusters, allowing for targeted updates to the security system.
- The system adapts based on the characteristics of events in the identified clusters, enhancing its effectiveness in responding to security threats.
Potential Applications: This technology can be applied in various industries where cloud computing is prevalent, such as cybersecurity, IT infrastructure management, and network security operations.
Problems Solved:
- Efficiently updating the configuration of a computer network security system in a cloud computing environment.
- Enhancing the system's ability to respond to security events by analyzing and clustering detected events.
- Improving the overall security posture of organizations operating in cloud environments.
Benefits:
- Enhanced security measures through targeted updates based on event characteristics.
- Improved efficiency in managing and updating network security configurations.
- Better protection against evolving cybersecurity threats in cloud computing environments.
Commercial Applications: The technology can be utilized by cybersecurity firms, cloud service providers, IT departments of large organizations, and any entity operating critical infrastructure in a cloud environment. It offers a proactive approach to network security management and threat response.
Prior Art: Readers interested in prior art related to this technology can explore research papers, patents, and industry publications on machine learning in network security systems and cloud computing environments.
Frequently Updated Research: Researchers in the field of cybersecurity and cloud computing regularly publish studies on the application of machine learning in network security operations. Stay informed about the latest advancements in this area to leverage cutting-edge technologies for enhancing security measures.
Questions about Machine Learning Techniques for Updating a Configuration of a Computer Network Security System Operating in a Cloud Computing Environment: 1. How does machine learning improve the efficiency of updating network security configurations in a cloud computing environment? 2. What are the key benefits of using trained ML models to analyze and cluster events in a security system for targeted updates?
Original Abstract Submitted
machine learning techniques for updating a configuration of a computer network security system operating in a cloud computing environment. the techniques include obtaining a plurality of datasets containing information about a respective plurality of events detected by the computer network security system in the cloud computing environment; generating, using at least one trained ml model, a plurality of signatures representing the plurality of events, the generating comprising processing the plurality of datasets using the at least one trained ml model to obtain the plurality of signatures; clustering the plurality of signatures to obtain signature clusters representing clusters of events in the plurality of events; identifying a particular event cluster from among the clusters of events; and updating the configuration of the computer network security system based on characteristics of events in the identified particular event cluster.
