8 citations · 13 across the 7 of their papers we have counts for
7 papers
TSI-GAN: Unsupervised Time Series Anomaly Detection using Convolutional Cycle-Consistent Generative Adversarial Networks
Shyam Sundar Saravanan, Tie Luo, Mao Van Ngo
Anomaly detection is widely used in network intrusion detection, autonomous driving, medical diagnosis, credit card frauds, etc. However, several key challenges remain open, such a…
Fast and Efficient Malware Detection with Joint Static and Dynamic Features Through Transfer Learning
Mao V. Ngo, Tram Truong-Huu, Dima Rabadi +2
In malware detection, dynamic analysis extracts the runtime behavior of malware samples in a controlled environment and static analysis extracts features using reverse engineering…
Long-Short History of Gradients is All You Need: Detecting Malicious and Unreliable Clients in Federated Learning
Ashish Gupta, Tie Luo, Mao V. Ngo +1
Federated learning offers a framework of training a machine learning model in a distributed fashion while preserving privacy of the participants. As the server cannot govern the cl…
Adaptive Anomaly Detection for Internet of Things in Hierarchical Edge Computing: A Contextual-Bandit Approach
Mao V. Ngo, Tie Luo, Tony Q. S. Quek
The advances in deep neural networks (DNN) have significantly enhanced real-time detection of anomalous data in IoT applications. However, the complexity-accuracy-delay dilemma per…
Coordinated Container Migration and Base Station Handover in Mobile Edge Computing
Mao V. Ngo, Tie Luo, Hieu T. Hoang +1
Offloading computationally intensive tasks from mobile users (MUs) to a virtualized environment such as containers on a nearby edge server, can significantly reduce processing time…
Contextual-Bandit Anomaly Detection for IoT Data in Distributed Hierarchical Edge Computing
Mao V. Ngo, Tie Luo, Hakima Chaouchi +1
Advances in deep neural networks (DNN) greatly bolster real-time detection of anomalous IoT data. However, IoT devices can hardly afford complex DNN models, and offloading anomaly…
Adaptive Anomaly Detection for IoT Data in Hierarchical Edge Computing
Mao V. Ngo, Hakima Chaouchi, Tie Luo +1
Advances in deep neural networks (DNN) greatly bolster real-time detection of anomalous IoT data. However, IoT devices can barely afford complex DNN models due to limited computati…