The invention provides an Android malicious
software detection
system based on multi-
modal feature fusion. The
system comprises a
system framework module and an optimization debugging module, wherein the
system framework module covers an
algorithm framework and a
website design framework. The
algorithm framework realizes a four-
modal training model, and performs data cleaning,
feature screening and fusion classification. A
website design framework is developed on the basis of Flask, and has specific functions and good impressions. A model is trained by using an AndroZoo
data set, and
integration testing and malicious APK detection are carried out. The experience optimization module comprises a
system integration and debugging process, a debugging process and a security and performance optimization scheme, relates to links such as environment building, interface testing, problem debugging, positioning and solving, performance analysis and optimization, user
acceptance testing and document updating, and adopts strict security measures and various performance optimization methods. By means of a unique multi-
modal feature fusion strategy and an advanced technology, the
performance index of the method is remarkably superior to that of other comparison methods, data deep features can be comprehensively captured, classification accuracy and stability are improved, and operation has specific environment requirements.