Research Article
A Network Intrusion Detection System Using Supervised Learning Techniques
@INPROCEEDINGS{10.4108/eai.16-5-2020.2304044, author={G Shalini and M Jaya Kumar and P Abhishek and M Dhamodaran}, title={A Network Intrusion Detection System Using Supervised Learning Techniques}, proceedings={Proceedings of the First International Conference on Advanced Scientific Innovation in Science, Engineering and Technology, ICASISET 2020, 16-17 May 2020, Chennai, India}, publisher={EAI}, proceedings_a={ICASISET}, year={2021}, month={1}, keywords={department of computer science and engineering dr t thimmaiah institute of technology kolar gold field karnataka india}, doi={10.4108/eai.16-5-2020.2304044} }- G Shalini
M Jaya Kumar
P Abhishek
M Dhamodaran
Year: 2021
A Network Intrusion Detection System Using Supervised Learning Techniques
ICASISET
EAI
DOI: 10.4108/eai.16-5-2020.2304044
Abstract
With rapid increase in the use of computer network in the fields of in-dustries, education, commerce, social media etc., makes the data security a need of hour. And one of the major threats for data security is the network intrusions, where the attacker intruded the network to steal confidential data’s like (pass-words, account details, etc.,), tries to stop the services or take control of the user devices. In order to stop this intrusion, the Network Intrusion Detection System is proposed (NIDS). The NIDS monitors the network and if any attack occurs, this system detects the attack and will alert the user about respective attack that occurred. Thus,these systemshelp in preventing intrusions in our networks. And for developing the most accurate NIDS, four different Machine Learning (ML) algorithms are used.The four algorithms used to build a high accuracy system are Random forest, SVM, Naïve Bayes, KNN are used. By identifying the algo-rithm with highest accuracy, the exact attack can be detected hence the required preventive measures can be taken.
