instrusion_dectection_of_imbalanced_network_using_machinelearning_and_deeplearning

In Imbalanced network traffic cyber attacks exhibits at a high degree and make difficult for Network Intrusion Detection System to ensure accuracy. By using Difficult Set Sampling Technique we tackle imbalance problem.
Here we use Machine Learning Algorithms such as Random Forest , Logistic Regression , Decision Tree , KNN , XGBoost and AdaBoost and Deep Learning Algorithms such as Alexnet and LSTM.
DeepLearning Algorithms such as Alexnet and LSTM we get 98-99% accuracy compared to Machine Learning Algorithms

Name		Name	Last commit message	Last commit date
Latest commit History 6 Commits
results		results
CNNLSTM.py		CNNLSTM.py
DTALG.py		DTALG.py
KDDTest+.csv		KDDTest+.csv
KDDTrain+.csv		KDDTrain+.csv
KNNALG.py		KNNALG.py
LRALG.py		LRALG.py
Main.py		Main.py
NeuralNetwork.py		NeuralNetwork.py
Predict.py		Predict.py
Preprocess.py		Preprocess.py
README.md		README.md
RFALG.py		RFALG.py
SVMalgo.py		SVMalgo.py
XGBoost.py		XGBoost.py
adaboost.py		adaboost.py
bg.png		bg.png
class.txt		class.txt
model.h5		model.h5
rate.jpg		rate.jpg

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