# MALICIOUS SERVER HACK

Malicious hacking of servers is the method of actively working to disable 
security systems with an intention of stealing information and taking down
the system.
Malicious server hacks are very common these days and are a threat to any organization.
This model is used to predict if a system can be hacked given various 
feature values.
The aim of the project is to classify feature vectors into two
classes, 0 for no hack and 1 for hack.

We have observed that the Random Forest Classifier is the best algorithm for a large dataset such as ours to predict the occurence of a hack.
It gives us an accuracy of 99%.






## About the Dataset

The dataset “Malicious Server Hack” was obtained from
kaggle. This dataset was published in 2020. It has over 11000 views The dataset had 17 attributes which were used to
predict a server’s hack and an output attribute which
classifies if the server was hacked. 

Our dataset has 18 columns and 2367 rows. The various columns
include incident id, date, type of computer, country, time
taken to enter the password, special character count etc. This dataset uses a classification model to predict if a
hack will happen or not. The parameters used to classify this
are anonymised logging parameters.

- Dataset link: https://www.kaggle.com/datasets/lplenka/malicious-server-hack


## Team Memebers

- Sharvaani Ravikumar Thoguluva : BL.EN.U4CSE20156
- Shruti P : BL.EN.U4CSE20157
- Suraj Gopinath : BL.EN.U4CSE20169
- Tanuja Konda Reddy : BL.EN.U4CSE20174