Taking on a classic challenge, NSL KDD. When training and test data come from differing probability distributions, training becomes difficult. We attempt to improve upon current results

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.

• scikit-learn
• xgboost
• numpy
• pandas

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