Based on the characteristics of APT's attack techniques, multiple detection methods should be utilized. By leveraging big data analysis technology, a continuous analysis of certain traffic behaviors is required to enhance the accuracy of localization.

The inchHeartNet Shield utilizes a multi-layered detection system across five dimensions—network, network forensics, Payload analysis, terminal behavior analysis, and terminal forensics—to monitor and secure against threats. This is achieved through advanced techniques including abnormal traffic detection, credibility checks, code analysis in a sandbox environment, comprehensive capture and analysis, and comprehensive analysis. It detects known threats while leveraging dynamic and static data analysis, cutting-edge big data, machine learning, and deep data mining techniques to identify and defend against APT attacks. The implementation process involves four data handling stages: Data Ingestion: collects multi-source data including traffic, file, and business types, extracts critical information from massive data through security checks; Data Storage: cleans and filters data, desensitizes and converts, normalizes and aggregates, and processes ETL to store structured, semi-structured, and unstructured data; Data Analysis: uses multi-dimensional real-time and batch visualization modeling for data analysis and associates single-point abnormal behavior to control the security situation and uncover potential threats, thereby reconstructing the APT attack chain; Data Presentation: uses visualization technology to dynamically present security analysis, achieving APT detection and early warning, accurately identifying and defending against APT attacks, and avoiding the loss of information assets.

APT Protection with Big Data Analytics, combined with threat intelligence, can identify and form new threats detected by big data analysis, determine their impact scope, and neutralize potential threats at their inception through threat sharing and联动 with the next-generation security network. Simultaneously, by collecting and refining external threat data, it can gather relevant threats for users, provide threat data to the situation awareness platform, construct a threat system, dynamically update data, and assist in strengthening the overall network security defense system, enhancing users' comprehensive security protection capabilities.