The invention discloses an intelligent edge security
threat real-time interception and early warning method, which relates to the technical field of
edge computing, and comprises the following steps: collecting operation data of
process behavior logs, network flow characteristics and hardware state parameters through a built-in lightweight sensor interface of edge equipment; redundant information is removed through
feature screening, and the data scale is simplified through dimension compression; deploying a lightweight neural
network model optimized by channel
pruning and knowledge
distillation at edge equipment, reasoning the preprocessed data in real time and outputting a
threat probability value; differential response is realized based on at least two levels of
threat judgment thresholds, high-risk threats trigger local
process isolation, network blocking and other rapid interception, and suspicious threats start early warning and directionally upload data to a cloud; and the cloud
collaboration system utilizes a high-precision deep neural network to secondarily verify suspicious data, generates a threat feature update
package, encrypts and distributes the threat feature update
package to each
edge device through federal learning, and realizes incremental update of an
edge model.