18472092. SELF LEARNING FIREWALL POLICY ENFORCER simplified abstract (Juniper Networks, Inc.)
Contents
- 1 SELF LEARNING FIREWALL POLICY ENFORCER
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 SELF LEARNING FIREWALL POLICY ENFORCER - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Unanswered Questions
- 1.11 Original Abstract Submitted
SELF LEARNING FIREWALL POLICY ENFORCER
Organization Name
Inventor(s)
Raja Kommula of Cupertino CA (US)
Ganesh Byagoti Matad Sunkada of Bengaluru (IN)
Tarun Banka of Milpitas CA (US)
Thayumanavan Sridhar of Sunnyvale CA (US)
Raj Yavatkar of Los Gatos CA (US)
SELF LEARNING FIREWALL POLICY ENFORCER - A simplified explanation of the abstract
This abstract first appeared for US patent application 18472092 titled 'SELF LEARNING FIREWALL POLICY ENFORCER
Simplified Explanation
The abstract describes a network system that uses machine learning to predict traffic patterns and detect anomalies in real-time.
- The network system includes processing circuitry and memories for storing instructions.
- Instructions cause the system to obtain traffic session metrics data, execute a machine learning model for traffic prediction, and compare predictions with real-time data to detect anomalies.
- An indication of the anomaly is generated based on the determination made by the system.
Potential Applications
This technology can be applied in various industries such as cybersecurity, network monitoring, and traffic management systems.
Problems Solved
1. Real-time detection of anomalies in network traffic. 2. Predicting traffic patterns accurately using machine learning models.
Benefits
1. Improved network security by detecting anomalies promptly. 2. Enhanced network performance through predictive traffic analysis.
Potential Commercial Applications
"Real-time Anomaly Detection and Traffic Prediction System for Network Security"
Possible Prior Art
One possible prior art could be traditional network monitoring systems that rely on static rules and thresholds to detect anomalies in network traffic.
Unanswered Questions
How does the system handle large volumes of traffic data in real-time?
The abstract does not provide details on the scalability of the system to handle high traffic loads.
What types of anomalies can the system detect?
The abstract does not specify the specific types of anomalies that the system can detect.
Original Abstract Submitted
An example network system includes processing circuitry and one or more memories coupled to the processing circuitry. The one or more memories are configured to store instructions which, when executed by the processing circuitry, cause the network system to obtain first traffic session metrics data and execute a machine learning model to determine a traffic prediction based on the first traffic session metrics data. The instructions cause the network system to obtain second traffic session metrics data and determine an anomaly in traffic based on a comparison of the traffic prediction and the second traffic session metrics data. The instructions cause the network system to, based on the determination of the anomaly, generate an indication of the anomaly.
