740 research outputs found
Machine Learning Radio-Frequency-Based Anomaly Detection for Ground Station and Satellite Telecommunication
Satellite-to-ground station telecommunication is a crucial aspect of satellite missions, representing a single point of failure of the entire space system.
Each failed contact is an issue for all satellite missions, leading to a potential data loss. The detection and forecasting of data transfer failures are critical challenges in satellite operations, given the unpredictability and variety of potential causes for such anomalies.
Considering the spectral waterfall plot the most appropriate tool to describe the anatomy of satellite contacts, an automatic waterfall analysis could help satellite mission operators, by promptly discovering potential data transmission failures between satellites and ground stations, and by forecasting anomaly behaviors.
The work reported in this paper exploits machine-learning models, trained with spectrogram waterfall diagrams to provide real-time and automatic anomaly detection of data transmission failures. Long-Short Term Memory and Deep learning models have been trained and validated, for anomaly detection and forecasting of contacts failures, with a dataset encompassing a semester’s worth of satellite contacts in both S-band and X-band.
With examples to identify the most appropriate model, this research will present practical outcomes and data-informed best practices in support of mission operators
An Anomaly Detection Method for Satellites Using Monte Carlo Dropout
Recently, there has been a significant amount of interest in satellite
telemetry anomaly detection (AD) using neural networks (NN). For AD purposes,
the current approaches focus on either forecasting or reconstruction of the
time series, and they cannot measure the level of reliability or the
probability of correct detection. Although the Bayesian neural network
(BNN)-based approaches are well known for time series uncertainty estimation,
they are computationally intractable. In this paper, we present a tractable
approximation for BNN based on the Monte Carlo (MC) dropout method for
capturing the uncertainty in the satellite telemetry time series, without
sacrificing accuracy. For time series forecasting, we employ an NN, which
consists of several Long Short-Term Memory (LSTM) layers followed by various
dense layers. We employ the MC dropout inside each LSTM layer and before the
dense layers for uncertainty estimation. With the proposed uncertainty region
and by utilizing a post-processing filter, we can effectively capture the
anomaly points. Numerical results show that our proposed time series AD
approach outperforms the existing methods from both prediction accuracy and AD
perspectives
Anomalien havaitseminen GNSS signaaleissa kompleksiarvoisilla LSTM neuroverkoilla
Today, Global Navigation Satellite Systems (GNSS) provide services that many critical systems [1] as well as normal users, need in everyday life. These signals are threatened by unintentional and intentional interference. The received satellite signals are complex-valued by nature, however, state-of-the-art anomaly detection approaches operate in the real domain. Changing the anomaly detection into the complex domain allows for preserving the phase component of the signal data.
In this thesis, I developed and tested a fully complex-valued Long Short-Term Memory (LSTM) based autoencoder for anomaly detection. I also developed a method for scaling of complex-numbers that forces both real and imaginary units into the range [-1,1] and does not change the direction of a complex vector. The model is trained and tested both in the time and frequency domains, and the frequency domain is divided into two parts: real and complex domain. The developed model’s training data consists only of clean sample data, and the output of the model is the reconstruction of the model’s input. In testing, it can be determined whether the output is clean or anomalous based on the reconstruction error and the computed threshold value.
The results show that the autoencoder model in the real domain outperforms the model trained in the complex domain. This does not indicate that the anomaly detection in the complex domain does not work; rather, the model’s architecture needs improvements, and the amount of training data must be increased to reduce the overfitting of the complex domain and thus improve the anomaly detection capability. It was also investigated that some anomalous sample sequences contain a few large valued spikes while other values in the same data snapshot are smaller. After scaling, the values other than in the spikes get closer to zero. This phenomenon causes small reconstruction errors in the model and yields false predictions in the complex domain
Anomaly Detection in Cloud Components
Cloud platforms, under the hood, consist of a complex inter-connected stack
of hardware and software components. Each of these components can fail which
may lead to an outage. Our goal is to improve the quality of Cloud services
through early detection of such failures by analyzing resource utilization
metrics. We tested Gated-Recurrent-Unit-based autoencoder with a likelihood
function to detect anomalies in various multi-dimensional time series and
achieved high performance.Comment: Accepted for publication in Proceedings of the IEEE International
Conference on Cloud Computing (CLOUD 2020). Fix dataset descriptio
Analysis of Artificial Intelligence based diagnostic methods for satellites
The growing utilization of small satellites in various applications has emphasized the need for reliable diagnostic methods to ensure their optimal performance and longevity. This master thesis focuses on the analysis of artificial intelligence-based diagnostic methods for these particular space assets.
This work firstly explores the main characteristics and applications of small satellites, highlighting the critical subsystems and components that play a vital role in their proper functioning.
The key components of this study revolve around Diagnosis, Prognosis, and Health Monitoring (DPHM) systems and techniques for small satellites. The DPHM systems aim at monitoring the health status of the satellite, detecting anomalies and predicting future system behavior. The reason why advanced DPHM systems are of interest for the space operators is the fact that they mitigate the risk of satellites catastrophic failures that may lead to service interruptions or mission abort. To achieve these objectives, a hybrid architecture combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks is proposed. This architecture leverages the strengths of CNNs in feature extraction and LSTM networks in capturing temporal dependencies. The integration of these two neural network architectures enhances the diagnostic capabilities and enables accurate predictions for small satellite systems.
Real data collected from an operational satellite is utilized to validate and test the proposed CNN-LSTM hybrid architecture. Based on the experimental results obtained, advantages and drawbacks of the exploitation of this architecture are discussed.The growing utilization of small satellites in various applications has emphasized the need for reliable diagnostic methods to ensure their optimal performance and longevity. This master thesis focuses on the analysis of artificial intelligence-based diagnostic methods for these particular space assets.
This work firstly explores the main characteristics and applications of small satellites, highlighting the critical subsystems and components that play a vital role in their proper functioning.
The key components of this study revolve around Diagnosis, Prognosis, and Health Monitoring (DPHM) systems and techniques for small satellites. The DPHM systems aim at monitoring the health status of the satellite, detecting anomalies and predicting future system behavior. The reason why advanced DPHM systems are of interest for the space operators is the fact that they mitigate the risk of satellites catastrophic failures that may lead to service interruptions or mission abort. To achieve these objectives, a hybrid architecture combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks is proposed. This architecture leverages the strengths of CNNs in feature extraction and LSTM networks in capturing temporal dependencies. The integration of these two neural network architectures enhances the diagnostic capabilities and enables accurate predictions for small satellite systems.
Real data collected from an operational satellite is utilized to validate and test the proposed CNN-LSTM hybrid architecture. Based on the experimental results obtained, advantages and drawbacks of the exploitation of this architecture are discussed
Cyber Security
This open access book constitutes the refereed proceedings of the 16th International Annual Conference on Cyber Security, CNCERT 2020, held in Beijing, China, in August 2020. The 17 papers presented were carefully reviewed and selected from 58 submissions. The papers are organized according to the following topical sections: access control; cryptography; denial-of-service attacks; hardware security implementation; intrusion/anomaly detection and malware mitigation; social network security and privacy; systems security
A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community
In recent years, deep learning (DL), a re-branding of neural networks (NNs),
has risen to the top in numerous areas, namely computer vision (CV), speech
recognition, natural language processing, etc. Whereas remote sensing (RS)
possesses a number of unique challenges, primarily related to sensors and
applications, inevitably RS draws from many of the same theories as CV; e.g.,
statistics, fusion, and machine learning, to name a few. This means that the RS
community should be aware of, if not at the leading edge of, of advancements
like DL. Herein, we provide the most comprehensive survey of state-of-the-art
RS DL research. We also review recent new developments in the DL field that can
be used in DL for RS. Namely, we focus on theories, tools and challenges for
the RS community. Specifically, we focus on unsolved challenges and
opportunities as it relates to (i) inadequate data sets, (ii)
human-understandable solutions for modelling physical phenomena, (iii) Big
Data, (iv) non-traditional heterogeneous data sources, (v) DL architectures and
learning algorithms for spectral, spatial and temporal data, (vi) transfer
learning, (vii) an improved theoretical understanding of DL systems, (viii)
high barriers to entry, and (ix) training and optimizing the DL.Comment: 64 pages, 411 references. To appear in Journal of Applied Remote
Sensin
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