# Intrusion Detection Systems
## How to run the code?
### For **Classical Machine Learning**
* Run `all.py` [[Link]](https://github.com/rahulvigneswaran/Intrusion-Detection-Systems/blob/master/all.py)
### For **Deep Neural Network (100 iterations)**
* Run `dnn1.py` for 1-hidden layer network and run `dnn1acc.py` for finding it's accuracy. [[Link]](https://github.com/rahulvigneswaran/Intrusion-Detection-Systems/tree/master/dnn)
* Run `dnn2.py` for 2-hidden layer network and run `dnn2acc.py` for finding it's accuracy. [[Link]](https://github.com/rahulvigneswaran/Intrusion-Detection-Systems/tree/master/dnn)
* Run `dnn3.py` for 3-hidden layer network and run `dnn3acc.py` for finding it's accuracy. [[Link]](https://github.com/rahulvigneswaran/Intrusion-Detection-Systems/tree/master/dnn)
* Run `dnn4.py` for 4-hidden layer network and run `dnn4acc.py` for finding it's accuracy. [[Link]](https://github.com/rahulvigneswaran/Intrusion-Detection-Systems/tree/master/dnn)
* Run `dnn5.py` for 5-hidden layer network and run `dnn5acc.py` for finding it's accuracy. [[Link]](https://github.com/rahulvigneswaran/Intrusion-Detection-Systems/tree/master/dnn)
### For **Deep Neural Network (1000 iterations)**
* Run `dnn1.py` for 1-hidden layer network and run `dnn1acc.py` for finding it's accuracy. [[Link]](https://github.com/rahulvigneswaran/Intrusion-Detection-Systems/tree/master/dnn1000)
* Run `dnn2.py` for 2-hidden layer network and run `dnn2acc.py` for finding it's accuracy. [[Link]](https://github.com/rahulvigneswaran/Intrusion-Detection-Systems/tree/master/dnn1000)
* Run `dnn3.py` for 3-hidden layer network and run `dnn3acc.py` for finding it's accuracy. [[Link]](https://github.com/rahulvigneswaran/Intrusion-Detection-Systems/tree/master/dnn1000)
* Run `dnn4.py` for 4-hidden layer network and run `dnn4acc.py` for finding it's accuracy. [[Link]](https://github.com/rahulvigneswaran/Intrusion-Detection-Systems/tree/master/dnn1000)
* Run `dnn5.py` for 5-hidden layer network and run `dnn5acc.py` for finding it's accuracy. [[Link]](https://github.com/rahulvigneswaran/Intrusion-Detection-Systems/tree/master/dnn1000)
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基于深度学习和机器学习的入侵检测系统.zip (75个子文件)
基于深度学习和机器学习的入侵检测系统
all.py.save 9KB
temp1.py 2KB
classical
predictedlabelDT.txt 607KB
expected.txt 607KB
.predictedprobaSVM-linear.txt.icloud 180B
accuclassical.py 4KB
predictedlabelAB.txt 607KB
predictedlabelLR.txt 607KB
predictedlabelNB.txt 607KB
predictedlabelRF.txt 607KB
predictedlabelSVM-rbf.txt 607KB
accclassical.py.save 707B
.predictedprobaNB.txt.icloud 172B
.predictedprobaKNN.txt.icloud 173B
predictedlabelSVM-linear.txt 607KB
.predictedprobaLR.txt.icloud 172B
.predictedprobaAB.txt.icloud 172B
.predictedprobaSVM-rbf.txt.icloud 177B
predictedlabelKNN.txt 607KB
.predictedprobaDT.txt.icloud 172B
.predictedprobaRF.txt.icloud 172B
.kddtrain.csv.icloud 162B
.idea
workspace.xml 3KB
misc.xml 193B
inspectionProfiles
profiles_settings.xml 174B
modules.xml 306B
.gitignore 47B
Intrusion-Detection-Systems.iml 284B
datasets
train_data.csv 653.65MB
test_data.csv 41.47MB
all.py.save.1 9KB
Evaluating Shallow and Deep Neural Networks for Network Intrusion Detection Systems in Cyber Security.pdf 171KB
dnn1000
dnn5.py 2KB
dnn4.py 2KB
kdd
binary
Testing.csv 0B
.Training.csv.icloud 162B
dnn1acc.py 3KB
dnn5test.py 4KB
dnn3.py 2KB
sample.py 2KB
dnn4test.py 4KB
dnn2test.py 3KB
dnn2.py 2KB
dnn1test.py 4KB
dnn3test.py 3KB
dnn1.py 2KB
dnn2test.py.save 2KB
README.md 2KB
.kddtest.csv.icloud 161B
dnn
dnn5.py 2KB
dnn4.py 2KB
kdd
binary
.Testing.csv.icloud 161B
.Training.csv.icloud 162B
dnn1acc.py 3KB
dnn5test.py 4KB
dnn3.py 2KB
dnn4test.py 4KB
dnn2test.py 3KB
dnn2.py 2KB
dnn1test.py 4KB
dnnres
expected.txt 607KB
.dnn3predicted.txt.icloud 169B
.dnn3probability.txt.icloud 171B
.dnn4predicted.txt.icloud 169B
.dnn5predicted.txt.icloud 169B
.dnn5probability.txt.icloud 171B
.dnn2probability.txt.icloud 171B
.dnn1predicted.txt.icloud 169B
.dnn4probability.txt.icloud 171B
.dnn2predicted.txt.icloud 169B
.dnn1probability.txt.icloud 171B
dnn3test.py 3KB
dnn1.py 2KB
dnn2test.py.save 2KB
all.py 9KB
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