The invention is suitable for the field of abnormal
data monitoring, and provides an abnormal
data monitoring method and
system based on
artificial intelligence, and the
system comprises a real-
time data flow collection module, a cross-level
correlation analysis module, a dynamic
baseline model building module, an abnormal behavior comparison module, and an abnormal
traceability module. According to the method, through real-
time data acquisition, cross-hierarchy analysis, dynamic modeling,
anomaly detection and deep
threat tracing, an efficient and intelligent anomaly monitoring
closed loop is constructed, and remarkable beneficial effects are presented. According to the method, firstly, real-
time data flow capture and cross-level
correlation analysis are utilized to ensure that interaction behaviors of multiple protocol levels in a network are comprehensively covered, and a complex abnormal mode is recognized in combination with context information; according to the multi-
dimensional analysis, the detection accuracy is remarkably improved,
false alarm and missing alarm are effectively reduced, meanwhile, an abnormal mark
data set with weights is generated, and high-
quality data support is provided for follow-up processes.