Network Anomaly Detection Using Random Forest Algorithm |
Author(s): |
| Sanjay Kumar. S , S.A Engineering College; Sachin. V, S.A Engineering College; Abishek.S, S.A Engineering College; Dr. Ahmed Mudassar Ali, S.A Engineering College |
Keywords: |
| Machine Learning, Random Forest Algorithm, Anomaly Direction, UNSW NB-15 Dataset |
Abstract |
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Our research is to reconcile the complication and serious threat possessed by network intrusion. The term network intrusion or anomaly designates the serious threat created by hackers, high jackers, spoofers, and etc that means the intruders who try to access our system with counterfeit intention. This complexion and threat is faced by all the networks and so to consolidate this complication we make use of an anomaly detection system. The subsisting detection systems work on the basis of rules and limitations. Our exertion provides a detection system which analyses the data packet and improves the accuracy utilizing machine learning random forest algorithm. This examination is extensively superior as it can detect the network anomalies with better accuracy than the existing systems. |
Other Details |
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Paper ID: IJSRDV9I20349 Published in: Volume : 9, Issue : 2 Publication Date: 01/05/2021 Page(s): 466-469 |
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