• Gaurav Yadav

With increasing use of networks, intrusions across the Internet have become a major threat in our world. This research study has been influenced by the different intrusion threats on internet and the ways to detect them. In this research we have studied and analyzed the famous network traffic data -NSL KDD dataset and its various features. The model aims to classify the network data into attack and non-attack and the attack class is further classified into DoS, Probe, R2L and U2R attack types. The work includes the classification of network traffic data by applying various classifier and analyze their accuracy. The model is being trained with different dataset sizes and have been evaluated on test data set. We have observed that the more we train the model, the more accurate classification results have been produced.

IDS, TCP/IP, KNN, security.

"Network Intrusion Detection - Comparison across classifiers", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.7, Issue 10, page no.2781-2783, October-2020, Available :http://www.jetir.org/papers/JETIR2010361.pdf

"Network Intrusion Detection - Comparison across classifiers", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.7, Issue 10, page no. pp2781-2783, October-2020, Available at : http://www.jetir.org/papers/JETIR2010361.pdf