• Vignesh Baalaji Shankar
• Sreehari. P. B
• Thilak Praveen. A
• Dr. S. Selvi

Generally, to create data for the Intrusion Detection System (IDS), it is necessary to set the real working environment to explore all the possibilities of attacks, which is expensive. Software to detect network intrusions protects a computer network from unauthorised users, including perhaps insiders. The intrusion detector learning task is to build a predictive model (i.e. a classifier) capable of distinguishing between "bad" connections, called intrusions or attacks, and "good" normal connections. To prevent this problem in network sectors have to predict whether the connection is attacked or not from KDDCup99 dataset using machine learning techniques. The aim is to investigate machine learning based techniques for better packet connection transfers forecasting by prediction results in best accuracy. To propose a machine learning-based method to accurately predict the DOS, R2L, UU2R, Probe and overall attacks by prediction results in the form of best accuracy from comparing supervised classification machine learning algorithms. Additionally, to compare and discuss the performance of various machine learning algorithms from the given dataset with evaluation classification report, identify the confusion matrix and to categorising data from priority and the result shows that the effectiveness of the proposed machine learning algorithm technique can be compared with best accuracy with precision, Recall and F1 Score.

Dataset, Machine Learning-Classification Method, Python, Prediction of Accuracy result, Machine Learning, Cyber Security, Network Attacks.

"A Machine Learning Model To Predict Network Attacks Based On Incoming Packet Features", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 3, page no.165-175, March-2019, Available :http://www.jetir.org/papers/JETIR1903C24.pdf

"A Machine Learning Model To Predict Network Attacks Based On Incoming Packet Features", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 3, page no. pp165-175, March-2019, Available at : http://www.jetir.org/papers/JETIR1903C24.pdf