Using Scikit decision tree classification algorithm to detect network intrusions. The dataset comes from KDD Cup 1999. The project is done by Zhaoqing Peng, Junyi Dai, Dastan, and Lingbin Ni.
Network intrusions classification using algorithms such as Support Vector Machine (SVM), Decision Tree, Naive Baye, K-Nearest Neighbor (KNN), Logistic Regression and Random Forest.
This project has been conducted under the supervision of Dr. Jinoh Kim and Dr. Donghwoon Kwon at Texas A&M University-Commerce. The research outcome are published in the proceeding of IEEE ICNC 2018 (http://www.conf-icnc.org/2018/), with the title of “An Empirical Evaluation of Deep Learning for Network Anomaly Detection”.
Using the 1998 DARPA Intrusion Detection Evaluation dataset I configured a Random Forest model for anomaly detection
Simple Implementation of Network Intrusion Detection System. KddCup'99 Data set is used for this project. kdd_cup_10_percent is used for training test. correct set is used for test. PCA is used for dimension reduction. SVM and KNN supervised algorithms are the classification algorithms of project. Accuracy : %83.5 For SVM , %80 For KNN
This project is an intrusion detection system which is trying to sniff the traffic packets and using ID3 classifier detects DoS attacks (Slowloris and Syn Flood). This source code has been written in python.
Python Script that sniffs network traffic on a Apache server and use anomaly detection methods to detect DDoS attack.
Analysis and preprocessing of the kdd cup 99 dataset using python and scikit-learn
