Neural Network Anomaly Detection with Chatbot Response
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Solution Overview
Problem
Conventional security solutions using artificial intelligence are limited in their ability to detect anomalies and provide timely and appropriate security measures, making it difficult for users to analyze and respond to anomaly symptoms effectively.
Innovation Solution
An anomaly detection method based on an artificial neural network that collects log data from users and systems, uses trained neural network models to detect anomalies, generate natural language messages, and facilitate communication through a chat room on a security application to provide information and enable security measures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional security solutions use AI to detect anomalies, then anomaly detection capability is improved, but the ability to provide timely and appropriate security measures deteriorates
Solution Approach 1:
A chatbot intermediary is introduced between anomaly detection and security response. The chatbot receives anomaly data from the AI detection system, processes it through natural language generation, and delivers actionable security recommendations to users in real-time, bridging the gap between detection and response
Solution Approach 2:
The system implements a feedback loop where the chatbot continuously monitors user interactions and anomaly patterns, refining security recommendations based on observed behaviors and outcomes, thereby improving both detection accuracy and response timeliness over time
2Adaptability or versatility
If conventional security solutions detect only data anomaly, then detection scope is limited, but system complexity is reduced
Solution Approach 1:
The chatbot system serves multiple functions: it acts as an anomaly analysis tool, a security recommendation engine, a user communication interface, and a knowledge base. This multi-functionality allows the system to handle diverse anomaly types without proportionally increasing complexity
Solution Approach 2:
The chatbot autonomously processes anomaly data, generates natural language explanations, and provides security recommendations without requiring human analysts for each incident. The system self-manages the complexity of analysis while simplifying user interaction
Data Source
AI summary
The present disclosure relates to an anomaly detection method based on an artificial neural network. The anomaly detection method based on the artificial neural network includes collecting first log data including first user log data and first system log data, and providing the collected first log data to a trained first artificial neural network model to perform anomaly detection for a plurality of users and systems.