20240048570. Device and Method for Generating a Response to an Attack in a Communication Network Using Machine Learning simplified abstract (NOKIA TECHNOLOGIES OY)
Device and Method for Generating a Response to an Attack in a Communication Network Using Machine Learning
Organization Name
Inventor(s)
Arunkumar Halebid of Bangalore (IN)
Device and Method for Generating a Response to an Attack in a Communication Network Using Machine Learning - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240048570 titled 'Device and Method for Generating a Response to an Attack in a Communication Network Using Machine Learning
Simplified Explanation
The abstract of this patent application describes a communication network device that is capable of predicting and detecting attacks based on data logs received from the network. The device generates graph representations of the data logs using a predefined schema. Attacks are detected by applying inference rules to the graph representations, while attacks are predicted using a graph neural network trained with subgraphs obtained from querying a graph representation of training data.
- The device is configured to predict and detect attacks in a communication network.
- Data logs received from the network are used to generate graph representations based on a predefined schema.
- Attacks are detected by applying inference rules to the graph representations of the data logs.
- Attacks are predicted using a graph neural network trained with subgraphs obtained from querying a graph representation of training data.
- The technology utilizes graph representations and inference rules to identify and respond to attacks in a communication network.
Potential Applications:
- Network security: This technology can be applied to enhance network security by predicting and detecting attacks in real-time, allowing for immediate response and mitigation.
- Intrusion detection systems: The device can be integrated into intrusion detection systems to improve their accuracy and effectiveness in identifying and responding to attacks.
- Cyber threat intelligence: The technology can contribute to the development of cyber threat intelligence systems by providing insights into attack patterns and trends.
Problems Solved:
- Timely attack detection: The device enables the timely detection of attacks by analyzing data logs and applying inference rules to identify suspicious patterns or behaviors.
- Attack prediction: By training a graph neural network with subgraphs obtained from training data, the device can predict attacks based on similarities to known attack patterns.
- Efficient response: The technology allows for a quick response to attacks upon prediction or detection, minimizing potential damage and reducing response time.
Benefits:
- Enhanced network security: The device improves the overall security of a communication network by predicting and detecting attacks, enabling proactive measures to be taken.
- Real-time threat response: With the ability to predict and detect attacks, the device facilitates real-time response, allowing for immediate action to mitigate the impact of attacks.
- Improved accuracy: By utilizing graph representations and inference rules, the technology enhances the accuracy of attack detection and prediction, reducing false positives and false negatives.
Original Abstract Submitted
in a communication network, a device is configured to predict attacks and detect attacks from data logs received from the network and generate a response to an attack upon prediction or detection of an attack. graph representations of data logs are generated based on a predefined schema. attacks are detected by applying inference rules to a graph representation of the data logs. attacks are predicted by using a graph neural network trained with subgraphs obtained by querying a graph representation of training data corresponding to normal traffic and attacks.
