Abstract: In order to solve the problem that things networking cloud storage data network camouflage bad information concealment causes information preprocessing difficulties, deep semantic understanding not accurate and sample disequilibrium, etc.，this paper put forward the things networking cloud storage data bad information detection based on Boundary-Incremental SVM algorithm This algorithm firstly takes mean initial clustering analysis based on mean and standard deviation of cloud storage data information for sample space training classification, and then puts all the sample classes euchdean distance traversal calculation to get son clustering center distance matrix between class and the clustering center adjacent boundary son clustering region,and then through the information content camouflage and selection principle of cloud storage information makes authenticity screening, using probability occurred in bad information in the false information as index, the data safety threshold and bad camouflage information template vector set of similarity threshold value as indexes,identifies the cloud storage information pseudo,finally carries on the incremental mode study, obtains each classification sample final optimal separating hyper plane, and will detect all kinds of bad camouflage information output System test proves that this algorithm can fast effective in things networking cloud storage data of camouflage information detection

Key words: True bogus information, Bad information camouflage, Information filtering, Similarity calculation, SVM