Network Intrusion Detection Using Clustering and Gradient Boosting

Published in: 2018 9th International Conference on Computing, Communication and Networking Technologies (ICCCNT)

Link to Publication https://ieeexplore.ieee.org/document/8494186

An unauthorized activity on the network is called network intrusion and device or software application which monitors the network parameters in order to detect such an intrusion is called network intrusion detection system (NIDS). With high rise in malicious activities on the internet, it is extremely important for NIDS to quickly and correctly identify any kind of malicious activity on the network. Moreover, the system must refrain from raising false alarms in case of normal usage detected as malicious. This paper proposes use of machine learning classification algorithms - XGBoost and AdaBoost with and without clustering to train a model for NIDS. The models are trained and tested using NSL KDD dataset and the results are an improvement over the previous works related to intrusion detection on the same dataset.

Requirements

• scikit-learn
• xgboost
• numpy
• pandas

To-dos

• Detailed running instructions
• Segregate code into 4 files for each experiment