INI-24: a dataset for combating interest flooding attack in named data networks using ISP-RocketFuel mapping engine
Lalitha Chinmayee M. Hurali, J. Sangeetha, Annapurna P Patil · Automatika · 2025
Named Data Networks (NDN) are a promising future Internet architecture optimised for content distribution, but remain vulnerable to Distributed Denial of Service (DDoS) attacks, such as Interest Flooding Attacks (IFA). A key limitation in developing robust defenses against IFAs is the lack of high-quality public datasets tailored for these attacks. To address this gap, we propose IFA in NDN using ISP topology (INI-24), a novel dataset specifically designed for IFA detection in NDN using ISP topology. We evaluated the INI-24 dataset against state-of-the-art datasets, such as IFAAll and SPOTIFAI, using various anomaly detection models. Our results show that the Isolation Forest model, trained on the INI-24 dataset, outperformed models trained on existing datasets, achieving an F1-score improvement of +0.41 and +0.49, respectively. With an overall accuracy of 0.996 and an F1-score of 0.9658, INI-24 demonstrates its potential as a benchmark dataset for advancing IFA detection research. These results demonstrate that the INI-24 dataset can serve as a practical benchmark for researchers and practitioners to develop and test more robust Interest Flooding Attack detection and mitigation solutions in real-world Named Data Networking deployments.