Analysis and Implementation of Machine Learning Approaches to DDoS Attack Detection
Project by Polygence alum D
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
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
D
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.”
About my mentor
“My mentor helped me find a good dataset and guided me with structuring my paper and approaching machine learning model programming.”
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