{"id":8992,"date":"2024-08-21T15:07:58","date_gmt":"2024-08-21T19:07:58","guid":{"rendered":"https:\/\/portfolios.cs.earlham.edu\/?page_id=8992"},"modified":"2025-04-14T15:34:52","modified_gmt":"2025-04-14T19:34:52","slug":"charles-bowen-rayner","status":"publish","type":"page","link":"https:\/\/portfolios.cs.earlham.edu\/index.php\/students\/2024-2\/cs488\/charles-bowen-rayner\/","title":{"rendered":"Charles Bowen-Rayner"},"content":{"rendered":"\n

Abstract<\/h2>\n\n\n\n

Machine learning is gaining prominence for network monitoring, yet current tools are often complex to understand and use. This proposal attempts to address this by developing an algorithm for network anomaly detection using libpcap and the random forest algorithm. This approach provides real-time anomaly detection by analyzing past network traffic from real-life datasets and employing machine learning techniques. Through various tested methods, the effectiveness of identifying various network anomalies will be analyzed. This study will highlight the potential of integrating libpcap and machine learning for scalable and adaptable network security solutions, contributing to improved threat detection in modern computing environments.<\/p>\n\n\n\n

Link to GitLab<\/h2>\n\n\n\n

https:\/\/code.cs.earlham.edu\/cdbowen21\/senior_capstone_cs_24<\/a><\/p>\n\n\n\n

Link to Paper<\/h2>\n\n\n\n
Charles Bowen Rayner NMT Paper<\/a>Download<\/a><\/div>\n\n\n\n

Data Architecture Diagram<\/h2>\n\n\n\n
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Poster<\/h2>\n\n\n\n
CBR-Network-Monitoring-Tool<\/a>Download<\/a><\/div>\n\n\n\n
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Software Demonstration Video<\/h2>\n\n\n\n
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