Dataset for a Systematic Mapping Study of Deep Learning-Based DDoS Detection in SDN and 5G/B5G Networks (2018–2024)

The dataset includes 205 peer-reviewed publications published between 2018 and 2024, retrieved from Scopus and IEEE Xplore using a structured search query. Each entry corresponds to a single study and includes bibliographic information (title, authors, year, publication venue etc.), as well as manually curated and derived attributes.

In addition to bibliographic metadata, the dataset provides structured annotations extracted from the title, abstract, and manual tags, including: learning_type (Supervised, Unsupervised, Reinforcement, Federated, Not specified; multi-label where applicable) dl_architecture (CNN, LSTM, BiLSTM, GRU, RNN, DNN/MLP, Autoencoder, GAN, Transformer, DBN, Echo State Network, Hybrid, Not specified; multi-label where applicable) network_context (SDN, 5G/6G, IoT/Edge, NFV, Not specified; multi-label where applicable) dataset_used (CICDDoS2019, InSDN, CIC-IDS-2017, CSE-CIC-IDS2018, NSL-KDD, UNSW-NB15, Edge-IIoTset, SDN-SlowRate-DDoS, ToN-IoT, ISCX, CICIoT2023, 5G-NIDD, Other/Custom, Not specified; multi-label where applicable) evaluation_setting (Offline, Testbed-Controller, Real-network, Not specified; multi-label where applicable) The annotations were generated through a semi-automated process combining script-based extraction and manual validation based on titles, abstracts, and manually assigned tags, without full-text analysis.