Evaluating the Effectiveness of Machine Learning Models in Detecting Network Attacks Using the Cicids 2017 Dataset
Student: Sekinat Oluwafunmilayo Aweda (Project, 2025)
Department of Computer Science
Kwara State University, Malete, Ilorin, Kwara State
Abstract
The increasing frequency and sophistication of cyberattacks have made network security a major concern. This study evaluates the effectiveness of various machine learning models in detecting network attacks using the CICIDS 2017 dataset. Algorithms such as Random Forest, SVM, CNN, RNN, and Gradient Boosting were compared using metrics like accuracy, precision, recall, and F1-score. Results show that Gradient Boosting achieved an impressive 99% accuracy, proving effective for intrusion detection. The findings highlight machine learning’s ability to enhance real-time network security and improve intrusion detection systems.
Keywords
For the full publication, please contact the author directly at: awedasekinat05@gmail.com
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Institutions
- Covenant Polytechnic, Aba, Abia State 1
- Covenant University, Canaan Land, Ota, Ogun State 4
- Crawford University of Apostolic Faith Mission Faith City, Igbesa, Ogun State 2
- Crescent University, Abeokuta, Ogun State 1
- Cross Rivers University of Technology, Calabar, Cross Rivers State 142
- Delta State Polytechnic, Ogwashi-Uku, Delta State 11
- Delta State Polytechnic, Otefe, Delta State 12
- Delta State University, Abraka, Delta State 138
- Ebonyi State University, Abakaliki, Ebonyi State 17
- Edo University, Iyamho, Edo State 10