"DDoS Attack Duration prediction"

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Authors: Mohamed Anis Sakka, Fahdah Alalyan, Wael Jaafar, Rami Langar
Cited by: 0

Abstract

"This dataset provides comprehensive network DDoS attack traffic and preprocessed temporal sequences for cyberattack duration prediction, derived from CICIoT2023 and augmented with real 5G O-RAN testbed captures. It includes 391 labeled attack sessions across five subcategories (UDP, ICMP, TCP, SYN, and SynonymousIP floods), with 49 original features refined to 21 predictive attributes. Each flow is augmented with a remaining_time target variable, enabling time-series forecasting through chronologically ordered sessions. Attacks are categorized as Short (1800s). Four federated learning partition configurations (IID, Non-IID by Label, Non-IID by Duration, and Fully Non-IID) support distributed learning research. Five real TCP SYN flood captures (80-120s) from a 5G O-RAN testbed are included for domain adaptation validation."

Fields of Study & Subjects

Network Security and Intrusion Detection Internet Traffic Analysis and Secure E-voting Network Packet Processing and Optimization

Publication details

Journal / source
IEEE DataPort
Publication date
26 February 2026
Language
EN
Document type
Dataset