Chúc mừng Sinh viên nhóm nghiên cứu UiTiOt có bài báo được chấp nhận tại Tạp chí quốc tế Q3 EAI Endorsed Transactions on Industrial Networks and Intelligent Systems

T6, 02/01/2026 - 17:43

SDG4

Bài báo: A Multimodal Swarm Learning Approach for DDoS Detection in Internet of Things Infrastructure

Sinh viên thực hiện:

- Phạm Nguyễn Hải Anh – ATTN2021 – Đồng tác giả

Giảng viên hướng dẫn:

- PGS. Lê Trung Quân.

- ThS. Nguyễn Khánh Thuật.

- ThS. Văn Thiên Luân.

Tóm tắt: The Internet of Things (IoT) has emerged as a foundational platform for driving intelligent solutions, playing a central role in the Fourth Industrial Revolution. Its potential lies in enabling seamless connectivity and real-time data exchange among diverse devices and systems, thereby powering advanced applications such as intelligent transportation, smart healthcare, precision agriculture, and automated manufacturing. These solutions promise to improve efficiency, optimize resource utilization, and enhance decision-making across various sectors. However, this potential is challenged by some issues, including security vulnerabilities, privacy concerns, and significant heterogeneity arising from the vast diversity of devices, communication protocols, and data formats. In this paper, we develop a multimodal deep learning solution to detect DDoS attacks on IoT infrastructure based on two data types: packet-based data and flow-based data. Firstly, the datasets containing packets labeled as benign or attack are processed into two branches: packet-based and flow-based features. Then, each branch is trained using two independent CNN models. Finally, the feature information extracted from both modalities is fused and fed into a concatenation-based classifier for DDoS attack detection. Experimental results on Edge-IIoTset and CiCIoMT2024 datasets indicate that the multimodal deep learning model within a decentralized machine learning architecture achieves performance comparable to centralized machine learning. In addition, our proposal is also robust to non-independent and identically distributed (non-IID) data in decentralized machine learning architecture.

Thông tin Tạp chí: EAI Endorsed Transactions on Industrial Networks and Intelligent Systems is open access, a peer-reviewed scholarly journal focused on ubiquitous computing, cloud computing, and cyber-physical system, all kinds of networks in large-scale factories, including a lot of traditional and new industries. The journal publishes research articles, review articles, commentaries, editorials, technical articles, and short communications with a quarterly frequency (four issues per year). This journal is co-organized, and managed by Duy Tan University, Vietnam in collaboration with Passage to ASEAN (P2A), an ASEAN Entity.