We propose using a residual network to classify encrypted network traffic by treating statistical data from packet headers as images. Residual networks solve gradient vanishing problems and allow for deeper neural networks that are better at classifying complex data. This general framework can model non-linear relationships and achieve more expressiveness and scalability than specific models for individual use cases. The goal is to help with network management, visibility and security as encrypted traffic grows.

1. Abdul Samadh and Madhusoodhana Chari S

2. With the growing adoption of encrypted traffic, Encrypted Network Traffic
Classification of different sorts has been very pivotal to network management,
visibility and security. With scaling networks and network usage the
continuous monitoring of networks has become very important.
We observe the flow statistical properties from the packet header. We
specifically propose using a residual network to treat statistical network data as
images and perform classification.

3.  Solves the vanishing gradient problem in deep
neural networks.
 Deeper neural networks are better at classifying
highly complex data.
 Gives us a general classification framework with
networking traffic modelling instead of using
specific models for specific use cases.
 Ability to model complex non-linear relations
and achieve more expressiveness and
generalization.
 Better scalability

5. 00000000 00000000 00000000 00101101
00000000 00000000 00000000 00000011
00000000 00000000 10010111 10100111
00000000 00000000 10101001 00000101
00000000 00000000 00000000 00000001
00000000 00000000 00000000 01001011