Detection of Malicious Intrusion Using R Based Random Forest Algorithm in Machine Learning
DOI:
https://doi.org/10.61841/2kkxvk43Keywords:
learning, decision table, random forest, network intrusionAbstract
The need to secure networks has increased as the number of people connecting to the network is increasing rapidly and using networks for storing or accessing critical information. An assessed and compared various machine learning algorithms and then proposed a system based on the best-performing algorithm. A system is an intrusion prediction system with a low error rate that can be implemented in the real world. The dataset utilized in the project comprises a lot of information to assess, remembering an assortment of reproduced intrusions for a military system condition. The dataset contains mainly the normal state, DDoS, and some other attacks. The system will not only predict a malicious network but will also indicate exactly what type of attack the network is on.
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References
[1] Review on Anomaly-based Network Intrusion Detection System Rafath Samrin Computer Science and Engineering ISL Engineering College Hyderabad, India D Vasumathi Computer Science and Engineering JNTUH Hyderabad, India.
[2] Method of Intrusion Detection Using Deep Neural Network by Jin Kim, Nara Shin, Seung Yeon Jo, and Sang Hyun Kim.
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[7] Machine Learning Techniques for Intrusion Detection Mahdi Zamani and Mahnush Movahedi.
[8] Network Intrusion Detection System Using Reduced Dimensionality
[9] Hierarchical Design-Based Intrusion Detection System for Wireless Ad Hoc Sensor Network.
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