text: This paper proposed to combine three kinds of data sources: real, simulated and unlabeled, to help solve "small" data issue occurring in packet stream. A directed information flow graph was constructed, a multi-headed network was trained by using Keras and GAN library. Its use on the packet sequence classification can archive comparable accuracy while relieve operation engineers from heavy background learning. The presentation of this paper can be improved. * With the missing citations as "(?)" and not clearly defined concepts, including property of function H (any function? convex?) in (3), full name of TCP/abbr of GAN when first appear, etc. reader might need to make guesses to follow. * P2: You can draw your audience by expanding the "related work" like a story: more background of GAN etc. and one or two highlight formula to help clear the idea * P3: What's the purpose of inserting "dummy packets to denote the timeout between two time stamps"? * P3: Help sell to "non-engineer" by maybe having image example or even plainer language to describe the meaning (deep difference/purpose) of "3 levels of feature engineering"; and when addressing features, mentioned as 1,2,3, while in Table 1, shown as Feature=0,1,2; * P6: section 4.2 mentioned "only metrics cared by operators", is this what you mean by "relieve operation engineers ...", and which is or how to decide the cutoff accuracy the engineers should make a Go or No Go decision?