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20202022
most citedAdaptive Anomaly Detection for IoT Data in Hierarchical Edge Computing

8 citations · 9 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CR20221 cited

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…

cs.LG2021

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…

cs.NI2020

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…

cs.LG2020

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…

cs.LG20208 cited

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…