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@article{181098,
        author = {Dattatray Waghole and Vishakha Vijay Bhosale and Sakshi Balasaheb Desai and Tushar Shamrao Dhangar and Prajwal Vikas Shelar},
        title = {Defense Strategies Against Interest Flooding in Vehicular Named Data Networks},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {1},
        pages = {4135-4141},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=181098},
        abstract = {This paper presents a machine learning-based detection framework for Interest Flooding Attacks (IFA) within Vehicular Named Data Networking (VNDN) environ-ments. With VNDN's shift toward content-centric communication, the network becomes vulnerable to malicious interest packet flooding, disrupting data flow and exhausting node resources. The proposed study employs a hybrid classification model integrating XGBoost, LSTM, and CNN, with a Deep Neural Net-work (DNN) serving as the meta-learner. Performance is evaluated based on two core metrics: detection accu-racy and classification latency. Using SUMO and ndnSIM, we simulate realistic vehicular scenarios and network behavior, generating diverse traffic data for training and validation. Results demonstrate that the ensemble model achieves over 97% accuracy while maintaining a low detection latency (~50ms), outper-forming individual classifiers. This suggests that stacked ensemble learning offers a reliable and efficient approach for enhancing security in VNDN systems, enabling real-time mitigation of IFAs in intelligent transportation networks.},
        keywords = {Vehicular Named Data Networking (VNDN), Interest Flooding Attack (IFA), Machine Learning (ML), En-semble Learning, Intelligent Transportation Systems (ITS), Network Security},
        month = {June},
        }