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All Journal Jurnal Ilmu Komputer JUTI: Jurnal Ilmiah Teknologi Informasi Jurnal Edukasi dan Penelitian Informatika (JEPIN) CESS (Journal of Computer Engineering, System and Science) JOIN (Jurnal Online Informatika) Briliant: Jurnal Riset dan Konseptual Abdi Masyarakat Aptekmas : Jurnal Pengabdian Kepada Masyarakat Jurnal Nasional Teknik Elektro dan Teknologi Informasi Jurnal Algoritma Control Systems and Optimization Letters
Pertiwi, Kharisma Monika Dian
Institut Teknologi Telkom Surabaya
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Deteksi Serangan DoS pada IoT Berbasis MQTT Menggunakan XGB dan PSO Dwi Azahra, Aisya; Monika Dian Pertiwi, Kharisma
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.2623

Abstract

The Internet of Things (IoT) is a technological innovation that enables physical devices to connect and communicate with one another through the internet, allowing them to exchange data automatically. This technology has been widely applied in various domains, such as smart homes and security systems. However, as the number of connected devices increases, the risk of cyberattacks also grows. One common type of attack is Denial of Service (DoS), an attempt to flood a system with excessive traffic, thereby disrupting communication between devices. This attack often exploits the MQTT protocol, which is popular in IoT environments due to its lightweight and efficient nature. This study aims to detect DoS attacks in MQTT-based IoT systems by implementing the Extreme Gradient Boosting (XGBoost) algorithm combined with feature selection using Particle Swarm Optimization (PSO). The dataset used consists of simulated MQTT traffic designed to resemble real-world conditions. The developed model is capable of classifying data into either normal or attack categories. The evaluation results demonstrate excellent performance, with precision ranging from 94.44% to 96.80%, recall from 99.63% to 99.97%, F1-scores between 96.95% and 97.81%, and an average accuracy of 99.89%. The main contribution of this research lies in integrating XGBoost with PSO on a realistic MQTT-based simulation dataset. This approach enhances both accuracy and computational efficiency, making it more suitable for resource-constrained IoT devices, and underscores the novelty of producing a DoS attack detection system that is accurate, efficient, and adaptive to real IoT network conditions.
Denial of Service (DOS) Attack Detection on MQTT Protocol Using the Random Forest Method Monika Dian Pertiwi, Kharisma; Azizi Hasibuan, Nurul; Putri Rahmawati, Dyah
JOIN (Jurnal Online Informatika) Vol 11 No 1 (2026)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v11i1.1784

Abstract

The Message Queuing Telemetry Transport (MQTT) protocol serves as a critical lightweight communication infrastructure for Internet of Things (IoT) systems. Still, it remains highly vulnerable to Denial of Service (DoS) attacks that compromise network availability and security. Despite extensive IoT security research, existing MQTT-based intrusion detection systems predominantly employ binary classification approaches and lack comprehensive multi-class attack differentiation capabilities, limiting their practical deployment in real-world scenarios. This study addresses this critical gap by developing a multi-class DoS attack detection system utilizing the Random Forest algorithm to simultaneously classify normal traffic, MQTT flooding attacks, and SYN flood attacks. The methodology encompasses four systematic stages: collecting an MQTT network traffic dataset containing 1,634,286 records across three attack categories through controlled simulations; performing rigorous data preprocessing for cleaning and normalization; strategically extracting 60 MQTT-specific attributes to identify attack signatures; and implementing Random Forest with optimized hyperparameters for multi-class classification. Experimental results demonstrate optimal performance using an 80:20 train-test split with 5-fold cross-validation, achieving 95.27% precision, 95.09% recall, 95.08% F1-score, and 95.09% accuracy. A comprehensive evaluation using macro and micro-averaged metrics confirms the model's ability to autonomously classify MQTT network traffic types with high accuracy and balanced performance across all attack categories, offering a practical security solution for MQTT-enabled IoT infrastructure.
Broad Learning System: A Derivation-Based Mathematical Formulation Saputra, Dimas Chaerul Ekty; Rahmawati, Dyah Putri; Pertiwi, Affifah Mutiara; Shafarin, Muhammad Ijaz; Pertiwi, Kharisma Monika Dian; Win, Thinzar Aung; Futri, Irianna; Safitri, Pima Hani
Control Systems and Optimization Letters Vol 4, No 1 (2026)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v4i1.294

Abstract

Broad Learning System is a wide learning framework that constructs nonlinear feature representations while enabling efficient model training through analytical solutions. This paper presents a derivation-based formulation of Broad Learning System that explains the mathematical structure underlying the learning process. The model constructs an expanded feature representation through feature mapping nodes followed by enhancement nodes that further enrich the learned representation. The learning problem is then expressed as a linear model in the constructed feature space, and the output weights are obtained using ridge regularized least squares optimization. This formulation allows the training process to be solved directly using matrix operations without iterative gradient based procedures. In addition, an incremental learning mechanism is introduced to enable efficient parameter updates when new samples or additional nodes are incorporated into the model. The presented formulation highlights how Broad Learning System combines nonlinear feature construction with computationally efficient closed form learning, providing a clear theoretical interpretation of the learning process.
Co-Authors Adityo, Kahil Akbar Bayu Ahmad Wali Satria Bahari Johan Ana Tsalitsatun Ni'mah Ardian Yusuf Wicaksono Azizi Hasibuan, Nurul Dahliar Ananda Dimas Chaerul Ekty Saputra Dwi Azahra, Aisya Fauzi, Muhammad Dzulfikar Futri, Irianna Jungjungan, Fadhlan Syahran Maulana, Rizky Fenaldo Muhajir, Daud Pertiwi, Affifah Mutiara Pratama, Moch. Andi Divangga Putri Rahmawati, Dyah Radityo Anggoro, Radityo Rahmawati, Dyah Putri Riza Akhsani Setyo Prayoga Rizky Fenaldo Maulana Safitri, Pima Hani Santik, Tita Arum Shela Shafarin, Muhammad Ijaz Siti Rochimah Vessa Rizky Oktavia Win, Thinzar Aung