Machine learning approach detects DDoS attacks
DOI:
https://doi.org/10.54654/isj.v1i15.850Keywords:
DDoSt, KNN, Decision Tree, Random Forest, SVMTóm tắt
Abstract— Denial of Service attacks have been
around since the dawn of the internet age. Along
with the development and explosion of the Internet,
denial of service attacks are also increasingly
powerful and become a serious threat in cyberspace.
The article aims to evaluate machine learning
algorithms: K-nearest neighbor (KNN) algorithm,
Decision Tree, Random Forest algorithm and
Support Vector Machine (SVM) on various metrics
in detecting DDoS attacks. The main objective of
the paper is to analyze the algorithms, collect data
and evaluate the effectiveness of the algorithms in
DDoS attack detection.
Tóm tắt— Tấn công từ chối dịch vụ đã xuất hiện từ
những năm khởi nguyên của thời đại internet. Song
hành cùng sự phát triển và bùng nổ của mạng
Internet, tấn công từ chối dịch vụ cũng ngày càng
mạnh mẽ và trở thành mối đe dọa nghiêm trọngtrên
không gian mạng. Bài báo hướng tới đánh giá các
thuật toán học máy: Thuật toán K láng giềng gần
nhất (K-nearest neighbor - KNN), cây quyết định
(Decision Tree), thuật toán rừng ngẫu nhiên
(Random Forest) và máy vector hỗ trợ (Support
Vector Machine - SVM) trêncác chỉ số đánh giá khác
nhau trong việc phát hiện các cuộc tấn công DDoS.
Mục tiêuchính của bài báo nhằm phân tích các thuật
toán, thu thập đánh giá dữ liệu và tiến hành so sánh
hiệu quả các thuật toánvào phát hiệntấn công DDoS.
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References
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