Browsing NTNU Open by Author "Lee, Ming-Chang"
Now showing items 1-18 of 18
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Assessing User Privacy in Virtual Assistant Devices via Passive Eavesdropping
Gustavsson, Lloyd Nicolay (Master thesis, 2023)I denne masteroppgaven ble det undersøkt hvorvidt passiv avlytting av nettverks-trafikk til og fra virtuelle assistentenheter ville kunne avsløre eventuelle brudd på personvern hos sluttbrukere. Metoden som er brukt i ... -
A Configurable and Executable Model of Spark Streaming on Apache YARN
Lin, Jia-Chun; Lee, Ming-Chang; Yu, Ingrid Chieh; Johnsen, Einar Broch (Peer reviewed; Journal article, 2020)Abstract: Streams of data are produced today at an unprecedented scale. Efficient and stable processing of these streams requires a careful interplay between the parameters of the streaming application and of the underlying ... -
DALC: Distributed Automatic LSTM Customization for Fine-Grained Traffic Speed Prediction
Lee, Ming-Chang; Lin, Jia-Chun (Chapter, 2020)Over the past decade, several approaches have been introduced for short-term traffic prediction. However, providing fine-grained traffic prediction for large-scale transportation networks where numerous detectors are ... -
Distributed Fine-Grained Traffic Speed Prediction for Large-Scale Transportation Networks based on Automatic LSTM Customization and Sharing
Lee, Ming-Chang; Lin, Jia-Chun; Gran, Ernst Gunnar (Chapter, 2020)Short-term traffic speed prediction has been an important research topic in the past decade, and many approaches have been introduced. However, providing fine-grained, accurate, and efficient traffic-speed prediction for ... -
DistTune: Distributed Fine-Grained Adaptive Traffic Speed Prediction for Growing Transportation Networks
Lee, Ming-Chang; Lin, Jia-Chun; Gran, Ernst Gunnar (Peer reviewed; Journal article, 2021)Over the past decade, many approaches have been introduced for traffic speed prediction. However, providing fine-grained, accurate, time-efficient, and adaptive traffic speed prediction for a growing transportation network ... -
Evaluation of K-Means Time Series Clustering Based on Z-Normalization and NP-Free
Lee, Ming-Chang; Lin, Jia-Chun; Stolz, Volker (Peer reviewed; Journal article, 2024)Despite the widespread use of k-means time series clustering in various domains, there exists a gap in the literature regarding its comprehensive evaluation with different time series preprocessing approaches. This paper ... -
GAD: A Real-Time Gait Anomaly Detection System with Online Adaptive Learning
Lee, Ming-Chang; Lin, Jia-Chun; Katsikas, Sokratis (Chapter, 2024) -
How Far Should We Look Back to Achieve Effective Real-Time Time-Series Anomaly Detection?
Lee, Ming-Chang; Lin, Jia-Chun; Gran, Ernst Gunnar (Chapter, 2021)Anomaly detection is the process of identifying unexpected events or abnormalities in data, and it has been applied in many different areas such as system monitoring, fraud detection, healthcare, intrusion detection, etc. ... -
Impact of Deep Learning Libraries on Online Adaptive Lightweight Time Series Anomaly Detection
Lee, Ming-Chang; Lin, Jia-Chun (Chapter, 2023)Abstract: Providing online adaptive lightweight time series anomaly detection without human intervention and domain knowledge is highly valuable. Several such anomaly detection approaches have been introduced in the past ... -
IoTective: Automated Penetration Testing for Smart Home Environments
Nordnes, Kevin; Lin, Jia-Chun; Lee, Ming-Chang; Chang, Victor (Chapter, 2024) -
NP-Free: A Real-Time Normalization-free and Parameter-tuning-free Representation Approach for Open-ended Time Series
Lee, Ming-Chang; Lin, Jia-Chun; Stolz, Volker (Chapter, 2023) -
PacketZapper: An Automated Collection and Processing Platform for IoT Device Traffic
Hedberg, Mathias Fredrik (Master thesis, 2023)Smarte enheter blir stadig vanligere i hjemmene våre og hjelper oss med ulike aspekter av hverdagen. Dette reiser spørsmål om sikkerhetskonsekvensene knyttet til den økende bruken av IoT-enheter i hjemmet. Å samle inn og ... -
PDS: Deduce Elder Privacy from Smart Homes
Lee, Ming-Chang; Lin, Jia-Chun; Owe, Olaf (Peer reviewed; Journal article, 2019)With the development of IoT technologies in the past few years, a wide range of smart devices are deployed in a variety of environments aiming to improve the quality of human life in a cost efficient way. Due to the ... -
RePAD: Real-time Proactive Anomaly Detection for Time Series
Lee, Ming-Chang; Lin, Jia-Chun; Gran, Ernst Gunnar (Chapter, 2020)During the past decade, many anomaly detection approaches have been introduced in different fields such as network monitoring, fraud detection, and intrusion detection. However, they require understanding of data pattern ... -
RePAD: Real-time Proactive Anomaly Detection for Time Series
Lee, Ming-Chang (Chapter, 2020)During the past decade, many anomaly detection approaches have been introduced in different fields such as network monitoring, fraud detection, and intrusion detection. However, they require understanding of data pattern ... -
ReRe: A Lightweight Real-time Ready-to-Go Anomaly Detection Approach for Time Series
Lee, Ming-Chang; Lin, Jia-Chun; Gran, Ernst Gunnar (Chapter, 2020)Anomaly detection is an active research topic in many different fields such as intrusion detection, network monitoring, system health monitoring, IoT healthcare, etc. However, many existing anomaly detection approaches ... -
RoLA: A Real-Time Online Lightweight Anomaly Detection System for Multivariate Time Series
Lee, Ming-Chang; Lin, Jia-Chun (Chapter, 2023)Abstract: A multivariate time series refers to observations of two or more variables taken from a device or a system simultaneously over time. There is an increasing need to monitor multivariate time series and detect ... -
UoCAD: An Unsupervised Online Contextual Anomaly Detection Approach for Multivariate Time Series from Smart Homes
Toor, Aafan Ahmad; Lin, Jia-Chun; Gran, Ernst Gunnar; Lee, Ming-Chang (Chapter, 2024)In the context of time series data, a contextual anomaly is considered an event or action that causes a deviation in the data values from the norm. This deviation may appear normal if we do not consider the timestamp ...