Rapid7, Inc. (20240333768). MACHINE LEARNING TECHNIQUES FOR UPDATING CONFIGURATION OF A COMPUTER NETWORK SECURITY SYSTEM simplified abstract
Contents
MACHINE LEARNING TECHNIQUES FOR UPDATING CONFIGURATION OF A COMPUTER NETWORK SECURITY SYSTEM
Organization Name
Inventor(s)
Pojan Shahrivar of Stockholm (SE)
MACHINE LEARNING TECHNIQUES FOR UPDATING CONFIGURATION OF A COMPUTER NETWORK SECURITY SYSTEM - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240333768 titled 'MACHINE LEARNING TECHNIQUES FOR UPDATING CONFIGURATION OF A COMPUTER NETWORK SECURITY SYSTEM
Simplified Explanation: This patent application discusses machine learning techniques for updating the configuration of a computer network security system in a cloud computing environment. The process involves obtaining datasets containing information about events detected by the security system, generating signatures representing these events using trained ML models, clustering the signatures to identify event clusters, and updating the security system configuration based on the characteristics of the events in these clusters.
Key Features and Innovation:
- Obtaining datasets with event information
- Generating signatures using ML models
- Clustering signatures to identify event clusters
- Updating security system configuration based on event characteristics
Potential Applications: This technology can be applied in various industries where cloud computing and network security are crucial, such as finance, healthcare, and e-commerce.
Problems Solved: This technology addresses the need for efficient and effective updating of network security configurations in a dynamic cloud computing environment.
Benefits:
- Improved security system configuration updates
- Enhanced threat detection and response capabilities
- Increased efficiency in managing network security in the cloud
Commercial Applications: The technology can be utilized by cloud service providers, cybersecurity companies, and organizations with large-scale cloud infrastructures to enhance their network security measures.
Prior Art: Researchers and professionals in the field of cloud computing, network security, and machine learning may find relevant prior art in academic journals, patent databases, and industry publications.
Frequently Updated Research: Stay updated on the latest advancements in machine learning algorithms, cloud security protocols, and network threat detection techniques to enhance the effectiveness of this technology.
Questions about Machine Learning Techniques for Updating a Configuration of a Computer Network Security System Operating in a Cloud Computing Environment: 1. How does this technology improve the efficiency of updating network security configurations in a cloud computing environment? 2. What are the key challenges faced in implementing machine learning techniques for network security configuration updates in the cloud?
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.
