The invention discloses an end network
information fusion detection method based on AI assistance. The end network
information fusion detection method comprises the steps that S01, terminal data are collected, and equipment fingerprints are constructed; S02, a unique equipment ID is generated through a Hash
algorithm, and ID preposition marks are used for distinguishing terminal types and establishing virtual identities for equipment; S03, multi-source
information fusion is carried out; s04, carrying out abnormal
feature evaluation through a multi-
source data cross validation model, and constructing a dynamic defense network through a bee colony distributed cooperation mechanism, S06, carrying out
information integration traceability tracking, and S07, carrying out causal chain reverse deduction; the method has the advantages that the updated multi-
source data cross validation model is higher in application
list, behavior log and flow feature
anomaly detection capacity and wider in range, the updated multi-
source data cross validation model is further applied to dynamic behavior modeling, known dangerous behavior data can be removed, and the method is suitable for dynamic behavior modeling. And repeated data accumulation is avoided, the data stock is reduced, and the problem of high maintenance and updating cost is solved.