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All Journal Emerging Science Journal Jurnal Nasional Teknik Elektro dan Teknologi Informasi Jurnal INFOTEL International Journal of Electrical Engineering and Intelligent Computing
Eueung Mulyana
School Of Electrical Engineering And Informatics, Institut Teknologi Bandung, Bandung 40132
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering Environmental Science

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Journal : emerging science journal

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Performance Evaluation of Significant Feature for Interest Flooding Attack Detection on Named Data Networking Jupriyadi; Syambas, Nana R.; Mulyana, Eueung
Emerging Science Journal Vol. 9 No. 5 (2025): October
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2025-09-05-07

Abstract

One of the internet architectures of the future that has advantages over the current system is Named Data Networking (NDN). However, Denial of Service (DoS) attacks, such as interest flooding attacks (IFA), can still disrupt the network. Detecting IFA attacks is crucial for preventing further damage. Several approaches to detection systems have been proposed, including a classification approach to detecting attacks with multiple detection parameters or features. However, the many detection system features that can be extracted from the network result in longer computation times for the classification algorithms. This research focuses on enhancing the detection of IFA by evaluating the features of the detection system and identifying significant features to improve detection accuracy and reduce computation time. We employed various feature selection algorithms, including information gain, wrapper naive Bayes, gain ratio, and correlation-based feature selection (CFS). The selected features are tested to detect attacks using several classification algorithms, including naive Bayes, random forest, J48, and Bayesian network. Our proposed method found only three essential features for detecting IFA from 18 features available, resulting in better detection accuracy and increasing by 47.8% the time to build the model. This study enhances NDN security while reducing computational cost, making real-time attack detection more feasible.
QLAF: Q-Learning Adaptive Forwarding for Disaster Emergency Communication in Named Data Networking Ratna Mayasari; Galih N. Nurkahfi; Nana R. Syambas; Eueung Mulyana
Emerging Science Journal Vol. 10 No. 2 (2026): April
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2026-010-02-03

Abstract

Reliable communication is essential for effective disaster response; however, conventional IP-based networks often fail when the network infrastructure is damaged. Disaster communication networks need adaptive forwarding strategies that maintain reliability under rapid topology changes, various link qualities, and resource constraints. This research proposes a Q-Learning-based Adaptive Forwarding (QLAF) strategy designed to enhance reliability in heterogeneous disaster emergency communication networks. QLAF implements reinforcement learning into the NDN forwarding plane, enabling each router to autonomously learn optimal forwarding faces based on multiple performance metrics: Round-Trip Time (RTT), throughput, and link stability. The proposed strategy was implemented in the Named Data Networking Forwarding Daemon (NFD) and evaluated using the MiniNDN emulator over a BRITE-generated 25-node disaster topology that integrates terrestrial, cellular, and satellite links. We compared QLAF and Adaptive Smoothed RTT-based Forwarding (ASF), Access strategy, and Self-Learning. Experimental results show that QLAF achieves a Packet Delivery Ratio (PDR) of 99.91%. These results show that QLAF gives a robust solution for reliability-sensitive disaster communication, guaranteeing high data delivery performance under unstable network conditions. However, its latency overhead limits its applicability to real-time scenarios.
Co-Authors Achmad Prajudin Sardju Andi Muhammad Ilyas Fahrizal Djohar Fierda Kurniacahya Ariefputra Galih N. Nurkahfi Jupriyadi Kholidiyah Masykuroh Muhammad Natsir Rahman Nana R. Syambas Ratna Mayasari Suciana Suciana Syambas, Nana R.