17956208. MACHINE LEARNING (ML) BASED SYSTEMS FOR AIR GAPPING NETWORK PORTS simplified abstract (Mellanox Technologies, Ltd.)
Contents
- 1 MACHINE LEARNING (ML) BASED SYSTEMS FOR AIR GAPPING NETWORK PORTS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 MACHINE LEARNING (ML) BASED SYSTEMS FOR AIR GAPPING NETWORK PORTS - 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 How does the system handle false positives in security threat detection?
- 1.11 What measures are in place to ensure the isolation of network ports does not disrupt network operations?
- 1.12 Original Abstract Submitted
MACHINE LEARNING (ML) BASED SYSTEMS FOR AIR GAPPING NETWORK PORTS
Organization Name
Inventor(s)
Ioannis (Giannis) Patronas of Piraeus (GR)
Tamar Viclizki Cohen of Herzliya (IL)
Vadim Gechman of Kibbutz Hulda (IL)
Dimitrios Syrivelis of Volos (GR)
Paraskevas Bakopoulos of Ilion (GR)
Nikolaos Argyris of Zografou (GR)
Elad Mentovich of Tel Aviv (IL)
MACHINE LEARNING (ML) BASED SYSTEMS FOR AIR GAPPING NETWORK PORTS - A simplified explanation of the abstract
This abstract first appeared for US patent application 17956208 titled 'MACHINE LEARNING (ML) BASED SYSTEMS FOR AIR GAPPING NETWORK PORTS
Simplified Explanation
The patent application describes a system for machine learning-based network resilience and steering, where data traffic is monitored across network ports to detect security threats and isolate affected ports.
- The system monitors data traffic across network ports.
- It determines data traffic patterns and uses machine learning to identify security threats.
- When a security threat is detected, the system isolates the affected network port from the rest.
Potential Applications
This technology could be applied in various industries where network security is crucial, such as finance, healthcare, and government sectors.
Problems Solved
This technology helps in proactively identifying and mitigating security threats in real-time, enhancing network resilience and minimizing potential damages from cyber attacks.
Benefits
The system provides an automated and efficient way to protect network infrastructure, reducing downtime and ensuring continuous operations.
Potential Commercial Applications
"Enhancing Network Security with Machine Learning-Based Resilience and Steering"
Possible Prior Art
Prior art in network security systems using machine learning algorithms to detect and respond to security threats may exist, but specific examples are not provided in this patent application.
Unanswered Questions
How does the system handle false positives in security threat detection?
The patent application does not detail how the system distinguishes between actual security threats and false alarms, which could impact the system's effectiveness in real-world scenarios.
What measures are in place to ensure the isolation of network ports does not disrupt network operations?
It is not clear from the patent application how the system ensures that isolating a network port does not cause disruptions to network operations or affect the connectivity of other devices on the network.
Original Abstract Submitted
Systems, computer program products, and methods are described herein for machine learning (ML) based network resilience and steering. An example system monitors data traffic across one or more network ports and determines a first data traffic pattern from the data traffic. The system further determines, via a ML subsystem, that the first data traffic pattern is indicative of a security threat to a first network port. In response to determining that the first data traffic pattern is indicative of the security threat to the first network port, the system further isolates the first network port from the one or more network ports.
