Bank of America Corporation (20240323203). ENHANCING HYBRID TRADITIONAL NEURAL NETWORKS WITH LIQUID NEURAL NETWORK UNITS FOR CYBER SECURITY AND OFFENSE PROTECTION simplified abstract
ENHANCING HYBRID TRADITIONAL NEURAL NETWORKS WITH LIQUID NEURAL NETWORK UNITS FOR CYBER SECURITY AND OFFENSE PROTECTION
Organization Name
Inventor(s)
Elvis Nyamwange of Little Elm TX (US)
ENHANCING HYBRID TRADITIONAL NEURAL NETWORKS WITH LIQUID NEURAL NETWORK UNITS FOR CYBER SECURITY AND OFFENSE PROTECTION - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240323203 titled 'ENHANCING HYBRID TRADITIONAL NEURAL NETWORKS WITH LIQUID NEURAL NETWORK UNITS FOR CYBER SECURITY AND OFFENSE PROTECTION
- Simplified Explanation:**
The patent application discusses enhancing traditional neural networks with liquid neural networks for cyber security and offense protection. The computing platform analyzes requests for access to organization data, flags potential threats, and uses extracted data to train a deep learning neural network to handle threats effectively.
- Key Features and Innovation:**
- Comparison of current requests to previous requests to identify potential threats - Extraction of data from requests to generate rules, threat detection algorithms, and training models - Training of a deep learning neural network to identify and handle threats to an enterprise organization
- Potential Applications:**
- Cyber security systems - Threat detection software - Enterprise data protection platforms
- Problems Solved:**
- Efficient identification and handling of potential threats to organization data - Enhanced security measures for enterprise organizations
- Benefits:**
- Improved threat detection capabilities - Enhanced cyber security for organizations - Efficient handling of potential threats
- Commercial Applications:**
- "Enhanced Neural Network for Cyber Security and Offense Protection: Improving Threat Detection and Response in Enterprise Organizations"
- Prior Art:**
Prior research in the field of neural networks, cyber security, and threat detection may provide insights into similar technologies and approaches.
- Frequently Updated Research:**
Stay updated on advancements in deep learning neural networks, liquid neural networks, and cyber security technologies to enhance the effectiveness of threat detection systems.
- Questions about Enhanced Neural Networks for Cyber Security and Offense Protection:**
1. How does the integration of liquid neural networks improve threat detection in comparison to traditional neural networks? 2. What are the potential limitations of using deep learning neural networks for cyber security applications?
Original Abstract Submitted
aspects of the disclosure relate to enhancing hybrid traditional neural networks with liquid neural networks for cyber security and offense protection. a computing platform may receive a request to access enterprise organization data. the computing platform may compare the current request to previous requests to determine whether a similar request was previously processed. if a similar request was not previously processed, the computing platform may flag the request as a threat and may analyze the request. the computing platform may extract data from the request and may use the extracted data to generate rules, threat detection algorithms, and training models. the computing platform may use the rules, threat detection algorithms, and training models to train a deep learning neural network to identify and handle threats to an enterprise organization.
