Thi Thuy Nga Dinh

• Pal, Kim Kristoffer; Eriksen, Aleksander Vanberg & Dinh, Thi Thuy Nga (2025). XGBoost Feature Selection for Multi-Class and Binary Classification on UNSW-NB15 Dataset. In IEEE (Eds.), 2025 IEEE International Conference on Consumer Electronics (ICCE). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9798331521165. doi: 10.1109/icce63647.2025.10930023. Full text in Research Archive Show summary

  Network intrusion detection systems (NIDS) are a critical part of ensuring the integrity and reliability of a network, which is essential in secure communication. To achieve this, an efficient NIDS with as low complexity as possible is preferred. Therefore, this paper presents a feature selection (FS) method that utilizes extreme gradient boosting (XGBoost) to train binary and multi-class machine learning (ML) algorithms on the 19 most important features in the UNSW-NB15 dataset. The FS method is evaluated on seven ML algorithms namely Artificial Neural Network (ANN), Logistic Regression (LR), k-Nearest-Neighbors (kNN), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and XGBoost. Results from both the multi-class and binary classifications showed that RF and XGBoost had the highest accuracy and Fl-score at (77.14%, 78%), (87.445%, 87%), (78.156%, 79%) and (87.527%, 87%), respectively. Compared to existing works, most ML algorithms demonstrate better or similar performance on the UNSW-NB15 dataset. In addition, the proposed FS method significantly reduces the running time for XGBoost and RF by approximately 80% and 48%, respectively in both binary and multi-class classifications.

• Gnanasivam, Sajepan; Tveter, Daniel & Dinh, Thi Thuy Nga (2024). Performance Evaluation of Network Intrusion Detection Using Machine Learning. In IEEE, . (Eds.), 2024 IEEE International Conference on Consumer Electronics (ICCE). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9798350324136. doi: 10.1109/ICCE59016.2024.10444363. Full text in Research Archive
• Singh, Ashay; Emon, Md Mohaiminul Islam & Dinh, Thi Thuy Nga (2024). Forecasting Indoor Air Quality Using Machine Learning Models. In IEEE, . (Eds.), 2024 IEEE International Conference on Consumer Electronics (ICCE). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9798350324136. doi: 10.1109/ICCE59016.2024.10444387. Full text in Research Archive
• Dinh, Nga & Haugen, Øystein (2022). Charging Scheduling Algorithm for Wireless-Powered Communication Networks, IECON 2022 – 48th Annual Conference of the IEEE Industrial Electronics Society. IEEE (Institute of Electrical and Electronics Engineers). ISSN 9781665480253. doi: 10.1109/IECON49645.2022.9968357. Full text in Research Archive
• Dinh, Nga; Tveito, Øystein & Haugen, Øystein (2021). Performance Evaluation for Energy Harvesting (EH)-based Sensor Nodes Under LoRaWAN Connectivity. In Bahk, Saewoong; Tran-Gia, Phuoc; Spiegel, Jan Van der & Quynh, Nguyen Xuan (Ed.), 2020 IEEE Eighth International Conference on Communications and Electronics (ICCE). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9781728154718. p. 64–69. doi: 10.1109/ICCE48956.2021.9352044. Full text in Research Archive Show summary

  Sustainable operation is one of the key challenges for Internet of Thing (IoT) sensor nodes because they are usually powered by limited battery power. Many IoT applications require the sensor nodes to operate reliably and consistently for a long period of time. Approaches to prolong the lifespan of sensor nodes include sleep-mode mechanism, harvesting energy from the environment (such as light, wind, water mills...) and from a dedicated power supply. Energy harvesting (EH) from nature has emerged as especially promising because it is clean, free and does not interrupt operation of the nodes. Long-Range Wide Area Network (LoRaWAN) can provide low-power, low-cost, and long-range communication for IoT nodes. In LoRaWAN, a node can extend its lifetime by using a deep-sleep mechanism. Even though the lifetime can be extended by the deep-sleep mechanism, it is expected to further extend the lifespan to reduce or eliminate the replacement of outage devices. Due to the potentials of LoRaWAN for IoT and the importance of EH approach to provide sustainable operation for IoT nodes, this paper focuses on using both EH and the deep-sleep mechanism in LoRaWAN to significantly prolong the lifetime of sensor nodes. In particular, the paper proposes a new power metric, called power gradient, to evaluate power performance of the network. The paper also estimates the performance of EH-based sensor nodes in terms of power gradient and lifetime. The proposed metric provides some insights on parameters configuration to achieve the desirable lifetime of EH-based nodes under LoRaWAN.

• Dinh, Thi Thuy Nga; Tveito, Øystein; Ha, Hoai Phuong & Anshus, Otto (2021). Implementation and Performance Analysis for Energy Harvesting-based Wireless Sensor Networks, IECON 2021 – 47th Annual Conference of the IEEE Industrial Electronics Society. IEEE (Institute of Electrical and Electronics Engineers). ISSN 9781665435543. doi: 10.1109/IECON48115.2021.9589684. Full text in Research Archive
• Dinh, Nga & Haugen, Øystein (2020). Analysis and Power Scheduling Algorithm in Wireless Powered Communication Networks, 16th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob 2020). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9781728197227. doi: 10.1109/WiMob50308.2020.9253379. Full text in Research Archive
• Abbas, Khurshid; Chockalingam, Sabarathinam; Dinh, Nga & Katta, Vikash (2020). A Prototype Tool for Distinguishing Attacks and Technical Failures in Industrial Control Systems. Norsk Informasjonssikkerhetskonferanse (NISK). ISSN 1893-6563. 13(3). Full text in Research Archive Show summary

  Critical Infrastructures (CIs) are governed by Industrial Control Systems (ICSs). Modern ICSs do not operate in isolation anymore, but they are connected to the Internet. This transformation introduced numerous advantages, however, there are a few drawbacks as well. Integration with the Internet has left ICS exposed to potential cyber-attacks. Additionally, ICSs could also encounter technical failures during operation. Consequently, it is crucial to distinguish between attacks and technical failures to initiate an appropriate response. There is a deficiency of robust technology to assist operators in distinguishing attacks and technical failures in an ICS environment. However, a framework is proposed to construct Bayesian Network (BN) models that would help to distinguish between attacks and technical failures for different observable problems in our previous work. There are tools available to implement such BN models, but these tools are not appropriate to use in an ICS environment. In order to address this limitation, this paper develops and demonstrates a prototype tool for swift identification of the major cause (Intentional Attack/Accidental Technical Failure) in case of an abnormal behaviour in a component of ICS. The proposed tool enables BN models to automatically update prior probabilities based on the historical data and/or expert knowledge corresponding to the application. The developed tool can be further evaluated and used to distinguish between attacks and technical failures during operation in CIs where ICSs are employed.

• Dinh, Nga & Ha, Hoai Phuong (2019). Advanced GTS Scheduling in IEEE 802.15.4 Networks for Industrial Application. In Kang, Joonhyuk (Eds.), Proceedings of the 16th IEEE Annual Consumer Communications & Networking Conference (CCNC). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9781538655535. p. 1–4. doi: 10.1109/CCNC.2019.8651809. Full text in Research Archive
• Dinh, Thi Thuy Nga & Ha, Hoai Phuong (2018). Resource Management for Improving Performance of IEEE 802.15.4-Based Home Automated Systems, GLOBECOM 2018. IEEE Sarnoff Symposium. ISSN 9781538649206. doi: 10.1109/GLOCOM.2018.8647804. Full text in Research Archive

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• Dinh, Thi Thuy Nga; Tveito, Øystein & Haugen, Øystein (2021). Performance Evaluation for Energy Harvesting (EH)-based Sensor Nodes Under LoRaWAN Connectivity. Full text in Research Archive
• Dinh, Nga & Haugen, Øystein (2020). Analysis and Power Scheduling Algorithm in Wireless Powered Communication Networks. Full text in Research Archive
• Dinh, Nga (2019). Resource Management for IEEE 802.15.4-based Smart HAN in Smart Grid. Full text in Research Archive
• Dinh, Thi Thuy Nga & Ha, Hoai Phuong (2018). Resource Management for Improving Performance of IEEE 802.15.4-Based Home Automated Systems. Full text in Research Archive
• Dinh, Nga & Sangsoon, Lim (2017). Device for wireless network and computer readable recording medium including communication method thereof. Full text in Research Archive
• Dinh, Nga & Sangsoon, Lim (2017). Devices for a wireless network system and communication methods thereof. Full text in Research Archive
• Dinh, Nga; P.N, Anthapadmanabhan & Anwar, Walid (2017). Optical Network Element and Method of Operating an Optical Network Element. Full text in Research Archive
• Dinh, Nga; Kanth, Lanka Madan; Prasad, Dayama Dwarka; Markus, Jung; Babu, Channa Ashok & Babu, Jampani Naga Ashok [Show all 8 contributors for this article] (2017). Terminal device and control method therefore. Full text in Research Archive
• Jie, Li; Aun, Chan Chien; Ka-Lun, Lee; Prasanth, Anthapadmanabhan N.; Dinh, Nga & Peter, Vetter (2013). Method and Apparatus for Managing Data Transmission In A Communication Network. Full text in Research Archive
• Anthapadmanabhan, N. Prasanth; Dinh, Nga; Walid, Anwar & Wijngaarden, Adriaan (2013). Analysis of a probing-based cyclic sleep mechanism for passive optical networks. Full text in Research Archive

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