publications

2024

1. ACM

   FedNIDS: A Federated Learning Framework for Packet-based Network Intrusion Detection System

   Quoc H. Nguyen, Soumyadeep Hore, Ankit Shah, and 2 more authors

   Digital Threats, Sep 2024

   Just Accepted

   Abs DOI PDF

   

   Network intrusion detection systems (NIDS) play a critical role in discerning between benign and malicious network traffic. Deep neural networks (DNNs), anchored on large and diverse datasets, exhibit promise in enhancing the detection accuracy of NIDS by capturing intricate network traffic patterns. However, safeguarding distributed computer networks against emerging cyber threats is increasingly challenging. Despite the abundance of diverse network data, decentralization persists due to data privacy and security concerns. This confers an asymmetric advantage to adversaries, as distributed networks face the formidable task of securely and efficiently sharing non-independently and identically distributed data to counter cyber-attacks. To address this, we propose the Federated NIDS (FedNIDS), a novel two-stage framework that combines the power of federated learning and DNNs. It aims to enhance the detection accuracy of known attacks, robustness and resilience to novel attack patterns, and privacy preservation, using packet-level granular data. In the first stage, a global DNN model is collaboratively trained on distributed data, and the second stage adapts it to novel attack patterns. Our experiments on real-world intrusion datasets demonstrate the effectiveness of FedNIDS by achieving an average F1 score of 0.97 across distributed networks and quickly disseminating novel attack information within four rounds of communication.

2023

1. IEEE

   Empirical Evaluation of Autoencoder Models for Anomaly Detection in Packet-based NIDS

   Soumyadeep Hore, Quoc H. Nguyen, Yulun Xu, and 3 more authors

   In 2023 IEEE Conference on Dependable and Secure Computing (DSC), Sep 2023

   DOI PDF
2. TechRxiv

   Class Label Conditioning Diffusion Model for Robust Brain Tumor MRI Synthesis

   Quoc Nguyen, Trung Le, Thang Nguyen, and 1 more author

   Oct 2023

   DOI

2020

1. IEEE

   Developing an Architecture for IoT Interoperability in Healthcare: A Case Study of Real-time SpO2 Signal Monitoring and Analysis

   Quoc H. Nguyen, Quang Dang, Chuan Pham, and 4 more authors

   In 2020 IEEE International Conference on Big Data (Big Data), Oct 2020

   DOI PDF

2016

1. ACM

   An Index Scheme for Similarity Search on Cloud Computing using MapReduce over Docker Container

   DT-Tri Nguyen, Chan Ho Yong, Xuan-Qui Pham, and 3 more authors

   In Proceedings of the 10th International Conference on Ubiquitous Information Management and Communication, Danang, Viet Nam, Oct 2016

   Abs DOI

   We consider the problem of similarity search over the large datasets in the distributed environment. The proposed framework employs the Vp-Tree algorithm that integrated on top of the MapReduce framework to achieve good performance as well as meet the scalability and fault tolerance requirements for the system while data scale up. Since VP-Tree algorithm was implemented initially for partition and searching data in the local disk access, we proposed a new approach to using it in the parallel environment. The key point of the Vp-Tree algorithm is that it distributed the similar data points into groups, thereby reducing number of data need to scan during the searching stage. Consequently, the response time of the entire system has been improved. Otherwise, we used an open source computer vision library OpenCV for detect the similarity among images in the dataset. We evaluate the performance of our proposed framework using a synthetic data to show the positive of our approach. The experiment shows that our proposed framework achieves 57% improvement in response time in comparison with running searching job in tradition Hadoop framework. We also compared our application running time on Docker container against VM-based environment. The result points out that deploy our system over Docker container provide higher performance than VM-based environment in term of response time.

2015

1. ACM

   Cloud-based real-time location tracking and messaging system: a child-care case study

   Cong-Thinh Huynh, Huu-Quoc Nguyen, Xuan-Qui Pham, and 2 more authors

   In Proceedings of the 9th International Conference on Ubiquitous Information Management and Communication, Bali, Indonesia, Oct 2015

   Abs DOI

   Cloud Computing represents an attractive and cost-efficient of server-based computing and application service provider models. Together with Cloud Computing development, we have witnessed the rapid increasing of mobile devices industry. Smart-phone is now a well-functioning system of GPS navigation, 3G/4G mobile network, wifi technology and much more. By using Cloud Computing, traditional mobile applications often involve with many Cloud Services such as online data storage, collaboration, real-time monitoring, web, email, push messaging, database processing, compute processing and so on. As the extension of mobile applications, real-time location tracking and messaging system has become particularly important. In this paper, we present a cloud-based real-time location tracking and messaging system (CRLTMS), that consists a cloud-based push messaging service, namely Google Cloud Messaging (GCM), web server, database and GPS navigation data. An Android-based child-care application case study shows that the effectiveness of the system, which enables the server tracks and communicates synchronously to smart-phones in real-time manner.

2. Elsevier

   Prediction-based energy policy for mobile virtual desktop infrastructure in a cloud environment

   Tien-Dung Nguyen, Pham Phuoc Hung, Tran Hoang Dai, and 3 more authors

   Information Sciences, Oct 2015

   Energy Efficient Data, Services and Memory Management in Big Data Information Systems

   Abs DOI

   Using cloud services from mobile devices has become a growing trend because of its mobility and convenience. However, mobile devices join and leave cloud services more frequently than traditional computers, which causes energy inefficiency in a cloud data center. Waste, in the form of energy and cooling requirements, particularly occurs when a mobile device disconnects from a service, but the cloud servers, known as virtual machines (VMs), continue running. The VMs should transition to lower-power states instead remaining active. However, transition to a lower-power state causes a service delay when users reconnect to the service because VMs in a lower-power state are not ready to serve. Therefore, an efficient energy policy must not only maximize energy savings but also minimize service delays. In this paper, we propose two approaches to energy efficiency: an Instant Energy Policy (IEP) that can quickly find an appropriate low-power state based on a predicted disconnection time and a Prediction-based Energy Policy (PrEP) that determines when to transition VMs to a low-power state and when to return them to the active state based on each users activity history. IEP predicts the unknown disconnection time using the multisize sliding windows workload estimation technique, which supports a non-stationary environment. This method can quickly obtain an energy policy, but it is limited when disconnection time fluctuates widely. PrEP presents an improved approach to achieve an optimal global result with respect to both energy consumption and service delay. Through simulations with a real-world dataset collected by the MIT Human Dynamics Lab, we show that PrEP provides approximately 20% power saving over the benchmark policies while guaranteeing minimal service delay.

3. IEEE

   Low cost real-time system monitoring using Raspberry Pi

   Huu-Quoc Nguyen, Ton Thi Kim Loan, Bui Dinh Mao, and 1 more author

   In 2015 Seventh International Conference on Ubiquitous and Future Networks, Oct 2015

   DOI PDF