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Analytical investigation of autoencoder-based methods for unsupervised anomaly detection in building energy data

APPLIED ENERGY 2018年 211卷 1123-1135页

作者： Fan, Cheng Xiao, Fu Zhao, Yang Wang, Jiayuan Shenzhen Univ Dept Construct Management & Real Estate Shenzhen Peoples R China Hong Kong Polytech Univ Dept Bldg Serv Engn Kowloon Hong Kong Peoples R China Zhejiang Univ Inst Refrigerat & Cryogen Hangzhou Zhejiang Peoples R China

Practical building operations usually deviate from the designed building operational performance due to the wide existence of operating faults and improper control strategies. Great energy saving potential can be realized if inefficient or faulty operations are detected and amended in time. The vast amounts of building operational data collected by the Building Automation System have made it feasible to develop data-driven approaches to anomaly detection. Compared with supervised analytics, unsupervised anomaly detection is more practical in analyzing real-world building operational data, as anomaly labels are typically not available. autoencoder is a very powerful method for the unsupervised learning of high-level data representations. Recent development in deep learning has endowed autoencoders with even greater capability in analyzing complex, high-dimensional and large-scale data. This study investigates the potential of autoencoders in detecting anomalies in building energy data. An autoencoder-based ensemble method is proposed while providing a comprehensive comparison on different autoencoder types and training schemes. Considering the unique learning mechanism of auto encoders, specific methods have been designed to evaluate the autoencoder performance. The research results can be used as foundation for building professionals to develop advanced tools for anomaly detection and performance benchmarking.

关键词： autoencoder Unsupervised data analytics Anomaly detection Building operational performance Building energy management

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Unsupervised Satellite Image Time Series Clustering Using Object-Based Approaches and 3D Convolutional autoencoder

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REMOTE SENSING 2020年第11期12卷 1816-1816页

作者： Kalinicheva, Ekaterina Sublime, Jeremie Trocan, Maria ISEP DaSSIP Team LISITE F-92130 Issy Les Moulineaux France Univ Paris 13 UMR 7030 CNRS LIPN F-93430 Villetaneuse France

Nowadays, satellite image time series (SITS) analysis has become an indispensable part of many research projects as the quantity of freely available remote sensed data increases every day. However, with the growing image resolution, pixel-level SITS analysis approaches have been replaced by more efficient ones leveraging object-based data representations. Unfortunately, the segmentation of a full time series may be a complicated task as some objects undergo important variations from one image to another and can also appear and disappear. In this paper, we propose an algorithm that performs both segmentation and clustering of SITS. It is achieved by using a compressed SITS representation obtained with a multi-view 3D convolutional autoencoder. First, a unique segmentation map is computed for the whole SITS. Then, the extracted spatio-temporal objects are clustered using their encoded descriptors. The proposed approach was evaluated on two real-life datasets and outperformed the state-of-the-art methods.

关键词： satellite image time series unsupervised learning clustering segmentation 3D convolutional network autoencoder

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autoencoder based Randomized Learning of Feedforward Neural Networks for Regression

Autoencoder based Randomized Learning of Feedforward Neural ...

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International Joint Conference on Neural Networks (IJCNN)

作者： Dudek, Grzegorz Czestochowa Tech Univ Dept Elect Engn Czestochowa Poland

ISBN: (纸本)9780738133669

Feedforward neural networks are widely used as universal predictive models to fit data distribution. Common gradient-based learning, however, suffers from many drawbacks making the training process ineffective and time-consuming. Alternative randomized learning does not use gradients but selects hidden node parameters randomly. This makes the training process extremely fast. However, the problem in randomized learning is how to determine the random parameters. A recently proposed method uses autoencoders for unsupervised parameter learning. This method showed superior performance on classification tasks. In this work, we apply this method to regression problems, and, finding that it has some drawbacks, we show how to improve it. We propose a learning method of autoencoders that controls the produced random weights. We also propose how to determine the biases of hidden nodes. We empirically compare autoencoder based learning with other randomized learning methods proposed recently for regression and find that despite the proposed improvement of the autoencoder based learning, it does not outperform its competitors in fitting accuracy. Moreover, the method is much more complex than its competitors.

关键词： autoencoder feedforward neural networks randomized learning algorithms

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autoencoder based Physical Layer Authentication for UAV Communications 97

Autoencoder based Physical Layer Authentication for UAV Comm...

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97th IEEE Vehicular Technology Conference (VTC-Spring)

作者： Senigagliesi, Linda Ciattaglia, Gianluca Gambi, Ennio Univ Politecn Marche Dept Informat Engn Ancona Italy

ISBN: (纸本)9798350311143

The use of flying Unmanned Aerial Vehicles (UAVs) for communications is becoming more and more widespread, especially in 5G and beyond networks. In such a context, detection and authentication of UAVs is assuming an increasingly important role. In this paper we show that it is possible to distinguish different drones which communicate with a fixed ground base station (BS) on the basis of their channel characteristics and of the micro-Doppler signature associated to the specific features of each UAV. An urban scenario is simulated where UAVs fly at a constant height and channels are affected by Additive White Gaussian Noise (AWGN) and fading. With the aim of helping the BS in its authentication task, we take advantage of a sparse autoencoder trained on the channel of the legitimate transmitter, while data coming from possible attackers are classified as anomalies. We prove that, with proper network training, low levels of false alarm and missed detection can be achieved, especially if the attacker has no line-of-sight link, and that the presence of micro-Doppler actually contribute to enhance the authentication performance.

关键词： autoencoder machine learning micro-Doppler PLA UAV communications

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autoencoder Feature Residuals for Network Intrusion Detection: Unsupervised Pre-training for Improved Performance 21

Autoencoder Feature Residuals for Network Intrusion Detectio...

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21st IEEE International Conference on Machine Learning and Applications (IEEE ICMLA)

作者： Lewandowski, Brian Paffenroth, Randy Worcester Polytech Inst Comp Sci Worcester MA 01609 USA Worcester Polytech Inst Data Sci Math Sci & Comp Sci Worcester MA 01609 USA

ISBN: (纸本)9781665462839

Network intrusion detection is a constantly evolving field as researchers and practitioners work towards keeping up with novel attacks and growing amounts of network data. To aid in this challenge researchers have been exploring the use of deep learning techniques such as neural networks in order to detect zero-day attacks and reduce the amount of manual analysis required when a network intrusion detection system alert is generated. Herein we use an unsupervised pre-training step in order to take advantage of autoencoder feature residuals. We show that autoencoder feature residuals can be used in place of or in addition to an original feature set as input to a neural network classifier to improve classification performance. Often in such problems, experts perform feature engineering to optimize classification performance. However, such data manipulation is expensive and time consuming. Our novel approach provides a path that can alleviate the need for manual feature extraction while "doing no harm". That is, if the provided features are in some sense optimal, then our methodology will not degrade the classification performance. However, if the provided features are inefficient, then we demonstrate that our methodology can substantially improve classification performance on a broad range of benchmark cybersecurity datasets. Another practical side effect of using autoencoder feature residuals comes to light by analyzing the potential data compression benefits they provide.

关键词： autoencoder network intrusion detection deep learning

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autoencoder based Communication System using Multi-Dimensional Constellations 35

Autoencoder based Communication System using Multi-Dimension...

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35th International Technical Conference on Circuits/Systems, Computers and Communications (ITC-CSCC)

作者： Lee, Hyungje Eom, Chahyeon Lee, Hyojin Lee, Chungyong Yonsei Univ Sch Elect & Elect Engn 50 Yonsei Ro Seoul South Korea Samsung Res Samsung Elect 56 Sungchon Gil Seoul South Korea

ISBN: (纸本)9784885523281

This paper proposes an autoencoder based multiple-input multiple-output (MIMO) communication system. The proposed autoencoder learns and optimizes for only line of sight (LOS) component of Rician channel. In addition, we adopt multi-dimensional constellation (MDC) in autoencoder, where it is obtained during learning process of autoencoder by adjusting hyperparameter. Simulation results show that our proposed autoencoder using MDC achieves better symbol error rate (SER) performance compared to conventional communication system which uses quadrature amplitude modulation (QAM) constellation. Furthermore, we confirmed that although proposed autoencoder is learned for only LOS component, it can be applied to random Rician flat fading channels with fading components and channel variation terms.

关键词： autoencoder multiple-input multiple-output multi-dimensional constellation Rician flat fading channel

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autoencoder-Based Tongue Shape Estimation During Continuous Speech 23

Autoencoder-Based Tongue Shape Estimation During Continuous ...

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Interspeech Conference

作者： Ribeiro, Vinicius Laprie, Yves Univ Lorraine CNRS Inria LORIA F-54000 Nancy France

Vocal tract shape estimation is a necessary step for articulatory speech synthesis. However, the literature on the topic is scarce, and most current methods lack adequacy to many physical constraints related to speech production. This study proposes an alternative approach to the task to solve specific issues faced in the previous work, especially those related to critical articulators. We present an autoencoder-based method for tongue shape estimation during continuous speech. An autoencoder is trained to learn the data's encoding and serves as an auxiliary network for the principal one, which maps phonemes to the shapes. Instead of predicting the exact points in the target curve, the neural network learns how to predict the curve's main components, i.e., the autoencoder's representation. We show how this approach allows imposing critical articulators' constraints, controlling the tongue shape through the latent space, and generating a smooth output without relying on any postprocessing method.

关键词： autoencoder tongue shape estimation phonemeto-articulation

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autoencoder Model Exploration for Multi-Layer Video Compression 10

Autoencoder Model Exploration for Multi-Layer Video Compress...

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10th European Workshop on Visual Information Processing (EUVIP)

作者： Cancellier, Luiz Henrique Grellert, Mateus Guntzel, Jose Luis da Silva Cruz, Luis A. Fed Univ Santa Catarina UFSC INE PPGCC Florianopolis SC Brazil Univ Coimbra Dept Elect & Comp Engn Coimbra Portugal Inst Telecomunicacoes Coimbra Portugal

ISBN: (数字)9781665466233

ISBN: (纸本)9781665466233

The use of autoencoder models for image and video compression have been explored by a number of works published in recent years. While those works perform the original data compression in a single layer, in this work we propose the use of autoencoder models in two-layered video coding. The adoption of multi-layer encoder provides scalability and allows us for decoupling the traditional video coding implementation from the NN solutions. By restricting the use of the Neural Network (NN) solution in the enhancement layer, it becomes possible to decode the base layer bitstream without the necessity of running the decoding process with the NN. We implemented and evaluated two autoencoder models: one using a symmetric encoder/decoder architecture, and an asymmetric alternative that employs more layers on the decoder side. The models were trained to compress residues for a scenario using All Intra encoding with spatial scalability. The Asymmetric model outperformed the Symmetric one by providing better compression rates and quality results, which is confirmed by the respective BD-Rate and BD-PSNR average results of -17.06% and 0.7dB, respectively.

关键词： Video Compression Layered Video Coding Neural Networks autoencoder Intra

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autoencoder-based Anomaly Detection in Smart Farming Ecosystem

Autoencoder-based Anomaly Detection in Smart Farming Ecosyst...

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9th IEEE International Conference on Big Data (IEEE BigData)

作者： Adkisson, Mary Kimmell, Jeffrey C. Gupta, Maanak Abdelsalam, Mahmoud Tennessee Technol Univ Dept Comp Sci Cookeville TN 38505 USA North Carolina A&T State Univ Dept Comp Sci Greensboro NC USA

ISBN: (纸本)9781665439022

The inclusion of Internet of Things (IoT) devices is growing rapidly in all application domains. Smart Farming uses IoT devices to increase efficiency and optimize farming operations. These devices can be used in a cloud or edge computing infrastructure which can provide remote control of watering and fertilization, real time monitoring of farm conditions, and provide solutions for more sustainable practices. These improvements to efficiency and ease of use come with added risks to security and privacy. Combining vulnerable IoT devices with the critical infrastructure of the agriculture domain broadens the attack surface for adversaries. Cyberattacks in a large coordinated manner could disrupt the economy of agriculture-dependent nations. To the sensors in a system, an attack may appear as anomalous behaviour. Additionally, there are possibilities of anomalies generated due to faulty hardware, issues in network connectivity (if present), or simply abrupt changes to the environment due to weather, human error, or other unforeseen circumstances. To make these systems more secure, it is imperative to detect such data discrepancies and trigger appropriate mitigation mechanisms. In this paper, we propose an anomaly detection model for Smart Farming using an unsupervised autoencoder machine learning model. We chose to use an autoencoder as our method of anomaly detection because it attempts to reconstruct normal data with a low reconstruction loss and anomalous data with a high loss. The high reconstruction loss value for a data point indicates that the data is not like the rest. Our model was trained and tested on data collected from our greenhouse test-bed. Our proposed autoencoder based anomaly detection method achieved 98.98% and took 262 seconds to train and has a detection time of .0585 seconds.

关键词： Smart Farming Anomaly Detection autoencoder Time-Series Data Grove Sensors Unsupervised Learning

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autoencoder Ensemble Method for Botnets Detection on IOT Devices 21

Autoencoder Ensemble Method for Botnets Detection on IOT Dev...

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21st IEEE International Conference on Machine Learning and Applications (IEEE ICMLA)

作者： Arroyo, Steven E. Ho, Shen Shyang Rowan Univ Glassboro NJ 08028 USA

ISBN: (纸本)9781665462839

Like anything else on the internet, IoT devices are very susceptible to cyber-attacks that could take out the device or install spyware. In this paper, we propose an anomaly detection solution driven by an autoencoder ensemble to detect botnets on IOT devices. In particular, the ensemble size is determined by hierarchical clustering of the features in the packet header. Moreover, one does not require an additional neural network to combine the decisions. The proposed approach is a more efficient solution for IOT problem setting and hence, overcomes the issue of lacking computational resources and memory on IOT devices, as well as run-time performance problems. Empirical results on two datasets, one from the 2016 Mirai botnet attacks on IoT devices and the other from Gafgyt malware attacks on various IOT devices, show the competitiveness and feasibility of our proposed solution.

关键词： autoencoder ensemble classifier anomaly detection IOT devices

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