The invention discloses a network
threat multi-
modal detection method based on a
large model, and belongs to the technical field of
network security, and the method comprises the steps: collecting three types of heterogeneous data of
NetFlow flow of a
network layer, a
system call chain sequence of a host layer and a protocol load of an
application layer, and carrying out the desensitization
processing and
feature coding to generate a unified
tensor format; the method comprises the following steps: through
network security threat intelligence and MITRE ATTamp; performing supervision
fine tuning on the
large model by using a CK
attack chain sample, and constructing a network
threat identification special model; cross-device behavior characteristics are extracted through a model self-attention mechanism, and a dynamic behavior map is constructed; and finally, comprehensively evaluating an
attack mode matching degree, a node
vulnerability mean value, historical alarm association and an
attack path risk by adopting a weighted fusion
algorithm, and triggering a high-confidence alarm when a comprehensive
score exceeds 0.8. According to the method, through multi-
modal data fusion and dynamic graph analysis, the detection precision and response efficiency of the complex attack chain are improved.