Article Per Year (5 Year)
p-Index From 2021 - 2026
1.325
P-Index
This Author published in this journals
All Journal Emerging Science Journal Jurnal Nasional Teknik Elektro dan Teknologi Informasi Jurnal INFOTEL International Journal of Electrical Engineering and Intelligent Computing
Eueung Mulyana
Departemen Teknik Elektro, Sekolah Teknik Elektro Dan Informatika, Institut Teknologi Bandung, Bandung, Jawa Barat 40132, Indonesia
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering Environmental Science

Published : 7 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 4 Documents
Search
Journal : emerging science journal

 1 
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.
Dual-Agent Q-Learning for Cross-Layer IEEE 802.11bd Optimization in Dense VANETs Galih Nugraha Nurkahfi; Suyoto; Agus Subekti; Budi Prawara; Ratna Mayasari; Andy Triwinarko; Nasrullah Armi; Eueung Mulyana; Nana Rachmana Syambas
Emerging Science Journal Vol. 10 No. 3 (2026): June
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2026-010-03-019

Abstract

Dense vehicular ad hoc networks face critical challenges in reliably delivering safety messages due to channel congestion, packet collisions, and interference. This study develops a dual-agent Q-learning framework for cross-layer IEEE 802.11bd optimization to improve latency and power efficiency while maintaining acceptable packet delivery ratios in dense traffic. We propose a decomposed architecture separating PHY-layer power control and MAC-layer beacon rate adaptation, with deterministic SINR-based MCS selection ensuring IEEE 802.11bd compliance. The framework is evaluated using a Python-based VANET simulator implementing the IEEE 802.11bd PHY/MAC stack with realistic SUMO mobility, multi-class background traffic, and omnidirectional/sectoral antennas across 20-90 vehicles/km densities. Results show dual-agent Q-learning reduces average latency by 44.6% (31.1ms to 17.2ms) and transmission power by 55% (15-20dBm to 9dBm) compared to static baselines, with acceptable 5-11% PDR reduction (94.2% to 88.6%). The approach converges within 8,500 episodes, significantly faster than single-agent Q-learning (12,500) and dual-agent DQN (14,000-35,000). This work introduces the first dual-agent tabular Q-learning for joint power-rate-MCS optimization in IEEE 802.11bd VANETs, demonstrating that agent decomposition reduces state-action complexity while enabling interpretable, fast-converging control suitable for sub-100ms vehicular applications.
Imbalanced Learning for Multiclass Fault Prediction in Optical Networks Using Enhanced Hybrid Sampling Rohmat Tulloh; Nana Rachmana Syambas; Eueung Mulyana
Emerging Science Journal Vol. 10 No. 5 (2026): October
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2026-010-05-08

Abstract

Fault prediction in optical networks faces the problem of class imbalance, where failure event data is much less than normal event data. Conventional classification models typically used in balanced or low-imbalance conditions tend to be biased toward the normal class. Sampling techniques such as oversampling tend to increase overlap. This causes high performance values in imbalanced classes not always to reflect the model's true ability to detect minority classes. This paper proposes a new framework called SMOTE-ENN-CatBoost-Optuna. This approach combines a hybrid sampling technique using synthetic minority oversampling (SMOTE) and edited nearest neighbors (ENN), controlled by Categorical Boosting (CatBoost), with hyperparameter tuning using Optuna. The proposed method dynamically adjusts the oversampling rate based on model error, followed by noise filtering to refine class boundaries. An iterative training strategy with Matthews correlation coefficient (MCC)-based convergence is further introduced to stabilize performance. To assess the proposed model's ability to adapt to varying levels of imbalance, imbalanced classes were grouped into three categories (medium, high, and extreme) based on the Imbalance Ratio (IR). Experimental results show that the proposed model consistently outperforms baseline methods. It achieves improvements of up to 0.9944 in MCC and 0.9951 in Macro F1-score. These results indicate the framework effectively enhances fault-detection reliability and maintains robust performance across most classes.
Co-Authors Achmad Prajudin Sardju Agus Subekti Andi Muhammad Ilyas Andy Triwinarko Budi Prawara Fahrizal Djohar Fierda Kurniacahya Ariefputra Galih N. Nurkahfi Galih Nugraha Nurkahfi Jupriyadi Kholidiyah Masykuroh Muhammad Natsir Rahman Nana R. Syambas Nana Rachmana Syambas Nasrullah Armi Ratna Mayasari Rohmat Tulloh Suciana Suciana Suyoto Syambas, Nana R.