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Forecasting of Network Intrusion by means of Efficient Machine Learning Algorithm
Published in Institute of Electrical and Electronics Engineers Inc.
2020
Abstract
It has been highly prescribed that our network needs to be locked as the use of storing data on the network has increased the number of snoopers. When an unauthorized or an unwanted activity occurs in a computer system then we refer it as network intrusion. It may allow a third party network to peek into our data, steal the data as well as change it. Therefore, the need to secure our network is highly recommended. To achieve this level of security, different machine learning algorithms are used and compared. The several algorithms which are compared are Naïve Bayes, Decision Table, K-Nearest Neighbor, Random Forest and Ad boost. The motive of this comparison is to identify the best performance algorithm. The following best algorithm will provide the high network attack accuracy. The dataset is produced which may contain a variety of intrusions. Our system will provide detection of the vicious network and establish the type of attack that has occurred in the system. Likewise, we can protect break off spiteful connection and provide security to our system. © 2020 IEEE.