https://elib.vku.udn.vn/handle/123456789/3877 2026-04-07T20:32:05Z https://elib.vku.udn.vn/handle/123456789/4046 Title: Proceedings of the 13th International Conference on Information Technology and Its Applications (CITA 2024) Authors: VKU Description: Proceedings of the 13th International Conference on Information Technology and Its Applications (CITA 2024) 2024-07-01T00:00:00Z https://elib.vku.udn.vn/handle/123456789/4045 Title: An Adaptive Cyber Deception Approach for Active Cyber-attack Defense Method based on Deep Reinforcement Learning Authors: Nguyen, Chi Toan; Nguyen, Hoang Hieu; Cao, Thi Bich Phuong; Phan, The Duy; Do, Hoang Hien Abstract: The diverse landscape of network models, including Software-Defined Networking (SDN), Cloud Computing (C2), and Internet of Things (IoT), is evolving to meet the demands of flexibility and performance. However, these environments face numerous security challenges due to cyber-attack complexity. Traditional defense mechanisms are no longer effective against modern attacks. Therefore, Defensive Deception (DD) is proposed as an active defense approach for deceiving attackers. Despite the optimized resource deployment of both Machine Learning (ML) and Deep Learning (DL), they necessitate the usage of pre-existing datasets that have been labeled. Our paper combines Deep Reinforcement Learning (DRL) and SDN technology to establish a novel strategic deception deployment method. This combination creates a powerful security solution that generates deceptive targets and resources to attract attackers, as a result, it provides improved visibility, threat detection, response capabilities, and threat intelligence. Our experiments are implemented on a simulated SDN-based network. The experimental results show that our approach gives significant effectiveness for deception resource allocation compared to random strategies. Description: Proceedings of the 13th International Conference on Information Technology and Its Applications (CITA 2024); pp: 343-354 2024-07-01T00:00:00Z https://elib.vku.udn.vn/handle/123456789/4044 Title: The Influence of Determinant Factors on Green Product Purchase Intention: The Role of Green Product Attitude Authors: To, Le Thanh Thao; Hoang, Hong Ngoc; Le, Thi Ngoc Anh; Tran, Nguyen Tra Giang; Nguyen, Uyen Nhi; Le, Phuoc Cuu Long Abstract: The environment always attracts attention, helping to motivate consumers to change their behavior in favor of green products. In Vietnam, there have been many studies on green product purchase intention but focus mainly on commercial aspects, consumer behavior, and the environment. This study examines the influence of social media and green brand positioning on green product purchase intention through the mediating role of customer attitude towards green products. The study used a quantitative approach to collect data, including questionnaires with survey methods. In the pilot test, data was collected from 150 respondents, followed by primary data collection from 502 participants in three major cities in Vietnam: Ha Noi, Da Nang, and Ho Chi Minh. The collected data are processed with a linear structural modeling method to test the research hypothesis. The results show that the study variables significantly influenced green product purchase intentions through attitudes toward green products. In addition, the result has many contributions in the academic area and supports businesses in designing effective strategies to promote green products, raise consumer awareness, and ultimately contribute to building sustainable and environmentally responsible consumption models in Vietnam. Description: Proceedings of the 13th International Conference on Information Technology and Its Applications (CITA 2024); pp: 329-342 2024-07-01T00:00:00Z https://elib.vku.udn.vn/handle/123456789/4043 Title: Identify Problems of Electrical Insulators using Multi-Task Learning Authors: Bui, Huy Trinh; Phan, Le Viet Hung; Le, Kim Hoang Trung; Nguyen, Huu Nhat Minh Abstract: Electrical insulators are important parts of electrical systems that often have some popular issues and require maintenance such as broken and dirty. Automatic detection using computer vision models for these problems could help to enhance the safety and continuity of the electrical operation in a proactive and timely manner with lower operational and maintenance costs. In this paper, we develop a multi-task model adopting Efficient-net as our base architecture following three branches for simultaneously three learning tasks such as identifying the type, cleanness, and broken status of the insulators. To train this multi-task model, we cleaned the dataset and performed the data augmentation of the practical dataset comprising 1500 images of electrical insulators provided by CPC Vietnam collected from pole-mounted surveillance cameras and drone survey flights. Throughout the extensive evaluation, the proposed muti-task models outperformed the single-task model by around 15-20% and demonstrated a robust design for identifying multiple problems of electrical equipment. Description: Proceedings of the 13th International Conference on Information Technology and Its Applications (CITA 2024); pp: 319-328 2024-07-01T00:00:00Z