ActivityRDI: A Centralized Solution Framework for Activity Retrieval and Detection Intelligence Based on Knowledge Graphs, Large Language Models, and Imbalanced Learning

network threat detection difficulty: Network threat detection is challenging due to complex attack activities and limited historically revealed threat data for learning.

enhancing threat detection methods: Existing analytics, machine learning, and artificial intelligence methods need enhancement for detecting network threats.

network threat detection difficulty: Network threat detection is challenging due to complex attack activities and limited historically revealed threat data for learning.

enhancing threat detection methods: Existing analytics, machine learning, and artificial intelligence methods need enhancement for detecting network threats.

ActivityRDI centralized framework: The paper proposes a centralized Activity Retrieval and Detection Intelligence framework for activity retrieval and detection intelligence.

multi-agent AI threat detection: The paper proposes a multi-agent AI solution for agile threat detection.

knowledge graph risk analysis: A knowledge graph is used to analyze changes in user activity patterns and calculate the risk of unknown threats.

imbalanced learning graph weighting: An imbalanced learning model is used to prune and weight the knowledge graph and calculate the risk of known threats.

LLM risk retrieval interpretation: A large language model retrieves and interprets risk associated with user activities from the knowledge graph and imbalanced learning model.

threat capture rate improvement:

accurate natural language risk interpretations:

deployment demonstration: