Passively categorize UDP dataset into attack and non-attack datasets.
We're using a simple perceptron, built with Tensorflow.
Although depth of the model is similar to that of a perceptron, the width of the model has been increased which lead us to better accuracy.
- Input: [ 4 nurons ]
- Hidden 1: [ 50 nurons ] FC (Fully Connected)
- Hidden 2: [ 50 nurons ] FC
- Hidden 3: [ 50 nurons ] FC
- Output: [ 2 nurons ] One-Hot
- Weight initialization: RandomNormal
- Activation:-
- All Hidden: ReLU
- Output: Softmax
- Cost function: CrossEntropy
- Optimizer: Adam (100 / 1,000,000 touple batch prop)
- 20 epochs of 10,000 iterations each
Input for
input tensor layermatches the below format:
4 nodes: [SOURCE IP] [DESTINATION IP] [SOURCE PORT] [DESTINATION PORT]
[SOURCE IP, DESTINATION IP] have been normalized:
- Actual IP addresses have been obfuscated by the source dataset
- Renormalization has been done to be able to train a model on IP addresses:
w * 1 + x * 2 + y * 3 + z * 4, wherew.x.y.zis an IP
Recommend you to develop a better normalization technique for IP address.
Output: 0 / 1, for isAttackPacket === true.
Dataset: 1 million UDP packets
Files inside /Data\ Sets are self-explanatory. MODEL_TEST contains saved model.
The MIT License (MIT)
Copyright 2019 Dhruvaraj nagarajan
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