Cyvers.AI Ltd. (20240235861). SYSTEM AND METHOD FOR MACHINE LEARNING BASED SECURITY INCIDENTS DETECTION AND CLASSIFICATION IN A BLOCKCHAIN ECOSYSTEM simplified abstract
SYSTEM AND METHOD FOR MACHINE LEARNING BASED SECURITY INCIDENTS DETECTION AND CLASSIFICATION IN A BLOCKCHAIN ECOSYSTEM
Organization Name
Inventor(s)
Deddy Lavid Ben Lolo of Yoqneam (IL)
Meir Badalov Dolev of Modiin (IL)
SYSTEM AND METHOD FOR MACHINE LEARNING BASED SECURITY INCIDENTS DETECTION AND CLASSIFICATION IN A BLOCKCHAIN ECOSYSTEM - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240235861 titled 'SYSTEM AND METHOD FOR MACHINE LEARNING BASED SECURITY INCIDENTS DETECTION AND CLASSIFICATION IN A BLOCKCHAIN ECOSYSTEM
The abstract describes a patent application where a management server collects on-chain and off-chain data for a blockchain ecosystem and generates a cross-chain graph representation. Machine learning models are applied to detect suspicious anomalies in the graph representation, triggering alerts for potential security incidents.
- The management server collects on-chain and off-chain data for a blockchain ecosystem.
- A cross-chain graph representation is generated, with nodes representing blockchain addresses and edges representing transactions.
- Machine learning models are used to detect suspicious anomalies in the graph representation.
- Alerts are generated for potential security incidents based on detected anomalies.
- A supervised machine learning model is applied to classify the type of security incident.
- Potential Applications:**
- Enhancing security in blockchain ecosystems. - Monitoring and detecting fraudulent activities in transactions. - Improving risk management in blockchain networks.
- Problems Solved:**
- Detection of suspicious anomalies in cross-chain transactions. - Classification of security incidents in blockchain ecosystems. - Enhancing overall security and risk management in blockchain networks.
- Benefits:**
- Early detection of security threats. - Improved risk mitigation strategies. - Enhanced trust and reliability in blockchain transactions.
- Commercial Applications:**
Title: "Blockchain Security Monitoring and Anomaly Detection Technology" This technology can be utilized by blockchain companies, financial institutions, and government agencies to enhance security measures in their blockchain networks. It can also be integrated into cybersecurity solutions for businesses operating in the blockchain space.
- Questions about Blockchain Security Monitoring and Anomaly Detection Technology:**
1. How does this technology contribute to the overall security of blockchain ecosystems?
- This technology enhances security by detecting suspicious anomalies in cross-chain transactions, enabling early intervention to prevent potential security incidents.
2. What are the potential implications of using machine learning models for security monitoring in blockchain networks?
- Machine learning models can significantly improve the efficiency and accuracy of security monitoring by quickly identifying and classifying security incidents in real-time.
Original Abstract Submitted
on-chain data as well as off-chain data for a blockchain ecosystem is collected by a management server. the management server generates a cross-chain graph representation based on the collected on-chain and off-chain data. the cross-chain graph representation includes a plurality of nodes representing blockchain addresses and a plurality of edges representing transactions made between at least a portion of the plurality of nodes. the management server applies one or more machine learning (ml) models to the cross-chain graph representation to detect suspicious anomalies in the cross-chain graph representation. upon determination that one or more suspicious anomalies have been detected in the cross-chain graph representation above a dynamic model-based threshold value, an alert indicating a security incident may be generated. in addition, a supervised ml model may be applied to the cross-chain graph representation for classifying the type of the security incident.
