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Sources: This documents sum up papers realated to my thesis. Points of interest: Idea Representation methods Labels/Categories Datasets Experiments?? Alogrithms Conclusions Critical Review Interesting references Labels/Categories Internet traffic categorization: VoIP, video, file transfer, chat, browsing Application identification: 10 classes representing VoIP and video applications over non-VPN encryption Protocol: VPN, TOR, Normal Experiments Evaluate all models on two few-shot datasets randomally selected with M=10,20,50 samples.

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Sources: This documents sum up papers realated to my thesis. Points of interest: Idea Representation methods Labels/Categories Datasets Experiments?? Alogrithms Conclusions Critical Review Interesting references Labels/Categories Internet traffic categorization: VoIP, video, file transfer, chat, browsing Application identification: 10 classes representing VoIP and video applications over non-VPN encryption Protocol: VPN, TOR, Normal Experiments Evaluate all models on two few-shot datasets randomally selected with M=10,20,50 samples.

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inter-arrival time normalize by 1 sec

packet length + direction (sign +/-) normalize by 1434 bytes

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2dflow's first n packets' size,time,direction

Important to mention, they splitted each udp after 15 sec to new Flow.
And each Tcp after a FYN..

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Google Documents, Google Drive, Google Music, Google Search, YouTube.

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VoIP, Streaming, File Transfer, chat, email

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Few-shot with mutlitasking + published QUIC dataset

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Few-shot with mutlitasking + published QUIC dataset

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Reformulate the traffic classification into a multi-task learning framework where bandwidth requirement and duration of a flow are predicted along with the traffic class.

kind of few shot - used 10,20,50,100 label per class

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40 bytes of headers when IP/PORT are masked

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40 bytes of headers when IP/PORT are masked

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AIM Chat, Email, Facebook, FTPS, Gmail, Hangouts, ICQ, Netflix, SCP, STFP, Skype, Spotify, Tor, Torrent, Vimeo, Voipbuster, Youtube

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Bittorrent, DNS, Finger, HTTP, HTTPS, SMTP, SSH, eDonkey, Whois

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Private protocols dataset recorded based port number

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Data representation study (2020 Springer Nature)

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Data representation study (2020 Springer Nature)

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Google Documents, Google Drive, Google Music, YouTube.

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inter-arrival time, packets direction, packets length of 20 packets

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2dflow's first n packets' size,time,direction

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VoIP, Video, File Transfer, chat

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It seemed they really struggled with ISCX dataset. The results are poor and they said most of it flows don't have enough packets.

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SAM just on 40 bytes . Too good results (98.62% and 98.93%)..

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SAM just on 40 bytes . Too good results (98.62% and 98.93%)..

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They took many packets of each flow.. Maybe they mixed the same flow in Train/Test.. IDK, it's too good to be true..

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R = packet size

G = packet direction

B = packet interarrival time

A = packet transport protocol (UDP/TCP)

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2dflow's first n packets' size,time,direction + protocol

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They did many experiments in order to test each method.
For e.g: ignoring some feature s.a packet_size and checking the results

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Behavioral based(graphlet)

Vectors(statistical features by Moore)

Time-series, Word Embedding

2d metrics (images)

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generated 1000 images out of M for training

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Path Optimization Prototypical Network

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BitTorrent, Facetime, FTP, Gmail, MySQL, Outlook, Skype, SMB, Weibo, WOW, Cridex, Geodo, Htbot, Minref, Neris, Nsisay, Shifu, Tinba, Virut, Zeus

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AIMChat, Email, Facebook Audio, Facebook Chat, Ftps Download, Ftps Upload, Gmail Chat, Hangouts Audio, Hangouts Chat, Hangouts Video, ICQ Chat, Netfix, Scp Download, Scp Upload, Sftp Download, Sftp Upload, Skype Audio, Skype Chat

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notice:28x28=784

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Convert a few (M=10,20,50 of each class) flows into images based packet content, embed into to high dimensional metric space with DL-CNN and Metric-learning than classify other flows.

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15/30/120 sec 1d-flow

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Represent flow as image and apply CNN

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Whatsapp web chat, Facebook chat and Google Hangout
chat

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Facebook-VoIP,
Facebook-Video,
Hangouts-VoIP,
Hangouts-Video,
Skype-VoIP, Skype-Video,
VoipBuster,
Netflix, Vimeo, Youtube

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VoIP, Video, File Transfer, chat, browsing

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2d-hist(pkt_time, pkt_size)
1500x1500 image