Network intrusions classification using algorithms such as Support Vector Machine (SVM), Decision Tree, Naive Baye, K-Nearest Neighbor (KNN), Logistic Regression and Random Forest.
Implemented a network intrusion detection system for a software defined network using Random Forest method for classification of port and flow statistics.
The given information of network connection, model predicts if connection has some intrusion or not. Binary classification for good and bad type of the connection further converting to multi-class classification and most prominent is feature importance analysis.
Analyzes network traffic and tells whether the query is normal or a type of attack. 3 classifiers are built and tested: Naive Bayes, Decision Trees, Random Forests, Followed by a complete visualization of results
The product is developed as a part of the final year project. It is aimed at providing an architecture and open source code to developers so that they can embed this into their applications to enhance the security. The services provided are top notch and cover the broad spectrum of computer and network security. All the features of the product involve the application of Data Mining and Machine Learning techniques onto the domain of Computer Security.
Welcome to my Individual Project for Codeup: Network Intrusion Detection. The goal was to build a machine learning model to detect anomalous behavior. Data courtesy of Kaggle.