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Using random forest algorithm to detect and prevent intrusion in a telecommunication network

 Department: Computer Science  
 By: usericon MIke_Tech_World  

 Project ID: 9631
   Rating:  (5.0) votes: 1
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   Price:₦5000
Abstract
The increasing dependence on telecommunication networks for communication and data exchange has made network security a critical concern. Telecommunication networks are constantly exposed to various forms of cyber threats, including unauthorized access, denial-of-service attacks, probing, and other malicious activities that can compromise network resources and user information. Traditional security mechanisms may not always effectively detect evolving and complex network intrusions, creating the need for intelligent and reliable intrusion detection techniques. This project focuses on the use of the Random Forest algorithm to detect and prevent intrusion in a telecommunication network. The proposed system applies machine learning techniques to network traffic data in order to identify patterns associated with normal and malicious activities. Relevant network traffic features are extracted and preprocessed before being used to train the Random Forest classification model. The trained model classifies network activities as either legitimate or intrusive and generates alerts when suspicious activities are detected. Appropriate preventive measures can then be applied to reduce the impact of identified threats. The performance of the proposed system is evaluated using standard network intrusion datasets based on metrics such as accuracy, precision, recall, F1-score, and detection rate. The results are expected to demonstrate the effectiveness of Random Forest in identifying different categories of network attacks while maintaining reliable classification performance. The study provides a machine-learning-based approach that can contribute to improving the security, reliability, and resilience of telecommunication networks....
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