Microsoft technology licensing, llc (20240211591). AUTOMATIC GRAPH-BASED DETECTION OF POTENTIAL SECURITY THREATS simplified abstract
Contents
AUTOMATIC GRAPH-BASED DETECTION OF POTENTIAL SECURITY THREATS
Organization Name
microsoft technology licensing, llc
Inventor(s)
Anisha Mazumder of Redmond WA (US)
Haijun Zhai of Bothell WA (US)
Daniel Lee Mace of Bellevue WA (US)
Yogesh K. Roy of Redmond WA (US)
Seetharaman Harikrishnan of Redmond WA (US)
AUTOMATIC GRAPH-BASED DETECTION OF POTENTIAL SECURITY THREATS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240211591 titled 'AUTOMATIC GRAPH-BASED DETECTION OF POTENTIAL SECURITY THREATS
Simplified Explanation: The patent application describes techniques for automatically detecting potential security threats using a graph-based approach.
Key Features and Innovation:
- Initialization of a Bayesian network using an association graph to establish connections among network nodes.
- Grouping of network nodes into clusters corresponding to different intents.
- Identification of patterns in the Bayesian network.
- Removal of redundant connections from the patterns.
- Assignment of scores to patterns based on historical data to indicate likelihood of a security threat.
- Automatic generation of an output graph containing potential security threats.
Potential Applications: This technology can be applied in cybersecurity systems to enhance threat detection capabilities.
Problems Solved: The technology addresses the challenge of efficiently identifying and prioritizing potential security threats in complex networks.
Benefits:
- Improved security threat detection.
- Automatic generation of actionable insights.
- Enhanced network protection.
Commercial Applications: Potential commercial applications include cybersecurity software development, network security services, and threat intelligence platforms.
Prior Art: Readers can explore prior research on Bayesian networks, graph-based security threat detection, and network clustering algorithms.
Frequently Updated Research: Stay updated on advancements in graph-based security threat detection, Bayesian network applications in cybersecurity, and machine learning for threat analysis.
Questions about Graph-Based Security Threat Detection: 1. How does the technology differentiate between different types of security threats? 2. What are the limitations of using Bayesian networks for security threat detection?
Question 1: What are the limitations of using Bayesian networks for security threat detection?
Answer 1: Bayesian networks may struggle with handling large amounts of data and complex network structures, which can impact the accuracy and efficiency of threat detection algorithms. Researchers are continuously working on optimizing Bayesian network models for better performance in security applications.
Original Abstract Submitted
techniques are described herein that are capable of performing automatic graph-based detection of potential security threats. a bayesian network is initialized using an association graph to establish connections among network nodes in the bayesian network. the network nodes are grouped among clusters that correspond to respective intents. patterns in the bayesian network are identified. at least one redundant connection, which is redundant with regard to one or more other connections, is removed from the patterns. scores are assigned to the respective patterns in the bayesian network, based on knowledge of historical patterns and historical security threats, such that each score indicates a likelihood of the respective pattern to indicate a security threat. an output graph is automatically generated. the output graph includes each pattern that has a score that is greater than or equal to a score threshold. each pattern in the output graph represents a potential security threat.
