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Analysis and Implementation of Machine Learning Approaches to DDoS Attack Detection

Project by Polygence alum Divij

Analysis and Implementation of Machine Learning Approaches to DDoS Attack Detection

Project's result

The CICDDoS2019 dataset was used in the research. 3 detection techniques were trained and tested using Decision Tree, Random Forest, and SVM (Linear kernel) models. Their effectiveness was evaluated and compared.

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Summary

The student will perform a background study on how DDoS attacks work, and will work with a real world dataset to perform data analysis and develop a detection technique.

Maria

Maria

Polygence mentor

PhD Doctor of Philosophy

Subjects

Computer Science, Social Science

Expertise

Social Sciences, Computational Social Sciences, Social Media, Graph Analysis, Depression on Social Media, Cyberbullying on Social Media, Hate Speech and Fake News Detection, Digital Epidemiology, Natural Language Processing, Artificial Intelligence, Machine Learning, Data Science, Threat Intelligence, Internet Security, Denial of Service Attacks, Network Traffic Analysis, Computer Science, Computer Networks

Review of mentor

My mentor helped me find a good dataset and guided me with structuring my paper and approaching machine learning model programming.

Divij

Divij

Student

Graduation Year

2024

Project review

Polygence offered a great chance to conduct a passion project in my field of interest and developed my skills in a key field, Machine Learning. This was an excellent experience above what I expected.