ATTACK DETECTION ON RAW LOG DATA WITH HELP OF ML

I would like to thanks VIMAL DAGA SIR and his whole team for helping and supporting us because of them we can learn this technology so easiy.
In this article we have developed python scripts that can comapre ur labelled log file with sample data and predict who all are attacker and all that things
in this label-raw-data.py is used to label raw data into labelled data after onece labelled data is save you can apply decision-tree-classifier or logistic-regression-classifier to get predicion of logs
I would also thanks my team mate Rohan and nikhil who help me in doing this project.
for label-raw-data.py use the following argument in cmd

python label-raw-data.py -l ./raw-http-logs-samples/access.log -d ./labeled-data-samples/access.csv

Name		Name	Last commit message	Last commit date
Latest commit History 7 Commits
labeled-data-samples		labeled-data-samples
raw-http-logs-samples		raw-http-logs-samples
Decission tree.png		Decission tree.png
Logistic Regression Classifiers.png		Logistic Regression Classifiers.png
README.md		README.md
creating labeled data2 from raw data log.png		creating labeled data2 from raw data log.png
creating labelled data 1 from raw log.png		creating labelled data 1 from raw log.png
decision-tree-classifier.py		decision-tree-classifier.py
label-raw-data.py		label-raw-data.py
labeled-data file 1 created.PNG		labeled-data file 1 created.PNG
labeled-data file 2 created.PNG		labeled-data file 2 created.PNG
logistic-regression-classifier.py		logistic-regression-classifier.py
utilities.py		utilities.py