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检索条件"主题词=Autoencoder"

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autoencoder based Physical Layer Authentication for UAV Communications 97

Autoencoder based Physical Layer Authentication for UAV Comm...

引用

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-Based Domain Learning for Semantic Communication with Conceptual Spaces 23

Autoencoder-Based Domain Learning for Semantic Communication...

引用

Wireless Telecommunications Symposium (WTS)

作者： Wheeler, Dylan Natarajan, Balasubramaniam Kansas State Univ Manhattan KS 66506 USA

ISBN: (纸本)9798350317893;9798350317886

Communication with the goal of accurately conveying meaning, rather than accurately transmitting symbols, has become an area of growing interest. This paradigm, termed semantic communication, typically leverages modern developments in artificial intelligence and machine learning to improve the efficiency and robustness of communication systems. However, a standard model for capturing and quantifying the details of "meaning" is lacking, with many leading approaches to semantic communication adopting a black-box framework with little understanding of what exactly the model is learning. One solution is to utilize the conceptual spaces framework, which models meaning explicitly in a geometric manner. Though prior work studying semantic communication with conceptual spaces has shown promising results, these previous attempts involve hand-crafting a conceptual space model, severely limiting the scalability and practicality of the approach. In this work, we develop a framework for learning a domain of a conceptual space model using only the raw data with high-level property labels. In experiments using the MNIST and CelebA datasets, we show that the domains learned using the framework maintain semantic similarity relations and possess interpretable dimensions.

关键词： semantic communication machine learning conceptual spaces autoencoder

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autoencoder ensembles for network intrusion detection 24

Autoencoder ensembles for network intrusion detection

引用

24th International Conference on Advanced Communication Technology (ICACT) - Artificial Intelligence Technologies toward Cybersecurity

作者： Long, Chun Xiao, JianPing Wei, Jinxia Zhao, Jing Wan, Wei Du, Guanyao CAS Chinese Acad Sci Comp Network Informat Ctr Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China

ISBN: (纸本)9791188428090

Machine learning methods have been widely used in the field of intrusion detection. However, most methods require labeled data sets, and the overhead is very high. Network data is often high-dimensional and has the problem of data imbalance, which makes many techniques unable to adapt to the real network environment. In this paper, we propose a network intrusion detection model based on autoencoder ensembles. This model uses a recursive feature addition algorithm to select the optimal subset of features, which can significantly reduce the training time of classifiers, and improve the performance of intrusion detection system. After feature selection, the feature subset is grouped, and then each group is mapped to an autoencoder. Multiple such autoencoders ensembles form the detection model. Only normal samples are used for training. The detection model is unsupervised, which improves the efficiency of detecting known and unknown attacks. The experimental results show that feature selection can effectively reduce training and detection time. Our model has high detection accuracy and strong adaptability.

关键词： autoencoder Feature Selection Machine Learning Network Intrusion Detection System Recursive Feature Addition

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autoencoder-BASED RADIO FREQUENCY INTERFERENCE MITIGATION FOR SMAP PASSIVE RADIOMETER

AUTOENCODER-BASED RADIO FREQUENCY INTERFERENCE MITIGATION FO...

引用

IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Owfi, Ali Afghah, Fatemeh Clemson Univ Holcombe Dept Elect & Comp Engn Clemson SC 29634 USA

ISBN: (纸本)9798350320107

Passive space-borne radiometers operating in the 1400-1427 MHz protected frequency band face radio frequency interference (RFI) from terrestrial sources. With the growth of wireless devices and the appearance of new technologies, the possibility of sharing this spectrum with other technologies would introduce more RFI to these radiometers. This band could be an ideal mid-band frequency for 5G and Beyond, as it offers high capacity and good coverage. Current RFI detection and mitigation techniques at SMAP (Soil Moisture Active Passive) depend on correctly detecting and discarding or filtering the contaminated data leading to the loss of valuable information, especially in severe RFI cases. In this paper, we propose an autoencoder-based RFI mitigation method to remove the dominant RFI caused by potential coexistent terrestrial users (i.e., 5G base station) from the received contaminated signal at the passive receiver side, potentially preserving valuable information and preventing the contaminated data from being discarded (1).

关键词： Interference Mitigation SMAP satellite Spectrum Coexistence RFI autoencoder

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autoencoder Using Kernel Method

Autoencoder Using Kernel Method

引用

IEEE International Conference on Systems, Man, and Cybernetics (SMC)

作者： Pei, Yan Univ Aizu Div Comp Sci Fukushima Ku Aizu Wakamatsu Fukushima 9658580 Japan

ISBN: (纸本)9781538616451

We propose a method that uses kernel method-based algorithms to implement an autoencoder. Deep learning-based algorithms have two characteristics, one is the high level data abstraction, the other is the multiple level data transformations and representations. The kernel method is one of the approaches that can be used in linear and non-linear transformations. It should be one of the implementations of these transformations in the deep learning. In this paper, the encoder part and decoder part of the autoencoder are implemented by kernel-based principal component analysis and kernel-based linear regression, respectively. As autoencoder is a basic structure and algorithm in deep learning, the proposed method can implement deep learning model and algorithm using duplicate structures. We use image data to evaluate our proposed method. The results show that kernel-based autoencoder can represent and restore image data, but the performance depends on the kernel function and its parameters' selection. We also discuss and analyse some open topics and works towards a study of kernel method-based deep learning.

关键词： kernel method deep learning kernel method-based deep learning autoencoder kernel-based principal component analysis kernel-based linear regression

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autoencoder Based Community Detection with Adaptive Integration of Network Topology and Node Contents 11th

Autoencoder Based Community Detection with Adaptive Integrat...

引用

11th International Conference on Knowledge Science, Engineering and Management (KSEM)

作者： Cao, Jinxin Jin, Di Dang, Jianwu Tianjin Univ Sch Comp Sci & Technol Tianjin 300350 Peoples R China Japan Adv Inst Sci & Technol Sch Informat Sci Nomi Ishikawa 9231292 Japan

ISBN: (纸本)9783319992471;9783319992464

Community detection plays an important role in understanding the structure and laws of social networks. Many community detection approaches have been proposed and focus on topological structure alone. In addition to topology, node contents exist in real-world networks, and may help for community detection. Recently, some studies try to combine topological structure and node contents. However, it is difficult to address an inherent situation in real-world networks, that is the mismatch between topological structure and node contents in term of community patterns. When considering both topology and content of networks, the performance of those community detection methods is often limited by this mismatch. Besides, networks are often full of nonlinear features, making those methods less effective in practice. In this paper, we present an adaptive method for community detection, which is reached by a graph regularized autoencoder approach. This new method introduces a novel adaptive parameter to achieve robust integration of the topological and content information when there exists the mismatch between those two types of information in term of communities. Experiments on both synthetic networks and real-world networks further indicate that the proposed new method exhibits more robust behavior and outperforms the leading methods when there exists the mismatch between topology and content.

关键词： Community detection Node contents autoencoder Mismatch

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autoencoder-Based Anomaly Detection in Network Traffic 25

Autoencoder-Based Anomaly Detection in Network Traffic

引用

25th International Conference on Computational Problems of Electrical Engineering (CPEE)

作者： Korniszuk, Krzysztof Sawicki, Bartosz Warsaw Univ Technol Fac Elect Engn Warsaw Poland

ISBN: (纸本)9798331506643;9798331506650

Due to the continuously increasing number of resources and data availability in the cloud, the threats related to the security of computer networks and IT systems are critical. Threat detection systems based on deep neural networks and anomaly detection are trained on data related to normal activity so that the network can recognize unusual patterns and behaviours in the event of an attack or an attempt to infiltrate a given IT infrastructure. This paper presents the results of developing a neural network based on an autoencoder for anomaly detection in network packet data. The network was trained on data from the HIKARI-2021 dataset. The autoencoder aims to learn representations of normal network traffic and associate this type of traffic with a minimal reconstruction error. The obtained results were compared with those achieved by authors of other works. High accuracy and sensitivity were achieved at the cost of rather low precision, resulting in many false-positive results. A simple algorithm based on a single threshold value proved efficient but limited in terms of effectiveness. This problem can be resolved by changing the method of calculating the individual components of the vector, using only a subset of features, and deriving multiple vectors, one for each class separately, which has been described and analyzed in more detail.

关键词： anomaly detection deep learning cybersecurity autoencoder threat detection

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autoencoder and Deep Neural Network based Energy Consumption Analysis of Marine Diesel Engine 19

Autoencoder and Deep Neural Network based Energy Consumption...

引用

19th IEEE International Conference on Mechatronics and Automation (IEEE ICMA)

作者： Zhang, Deft Wang, Kangli Gao, Jianfeng Che, Xiuming Tianjin Univ Technol Maritime Coll Tianjin Peoples R China Northern Nav Serv Ctr Tianjin AtoN Div Tianjin Peoples R China

ISBN: (数字)9781665408530

ISBN: (纸本)9781665408530;9781665408523

In order to improve the intelligent energy efficiency management of ships, evaluate the fuel utilization efficiency of marine diesel engine. In this paper, a fuel consumption model of marine diesel engine based on autoencoder and deep neural network is established, and the autoencoder is used to perform nonlinear dimensionality reduction on the data to obtain more valuable data features, thereby improving the accuracy of the model. The model is verified and compared using the sailing parameters, environmental parameters and fuel consumption of the actual ship during normal sailing. The accuracy rate of the model established in this paper reaches 95.19%, and the results show that the model in this paper can meet the prediction and evaluation analysis of the energy consumption of the marine diesel engine.

关键词： Ship Energy Efficiency Deep Neural Network autoencoder

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autoencoder and its various variants

Autoencoder and its various variants

引用

IEEE International Conference on Systems, Man, and Cybernetics (SMC)

作者： Zhang, Sufang Zhai, Junhai Chen, Junfen He, Qiang China Meteorol Adm Hebei Branch China Meteorol Adm Training Ctr Baoding 071000 Peoples R China Hebei Univ Coll Math & Informat Sci Key Lab Machine Learning & Computat Intelligence Baoding 071002 Peoples R China Beijing Univ Civil Engn & Architecture Sch Sci Beijing 100044 Peoples R China

ISBN: (纸本)9781538666500

The concept of autoencoder was originally proposed by LeCun in 1987, early works on autoencoder were used for dimensionality reduction or feature learning. Recently, with the popularity of deep learning research, autoencoder has been brought to the forefront of generative modeling. Many variants of autoencoder have been proposed by different researchers and have been successfully applied in many fields, such as computer vision, speech recognition and natural language processing. In this paper, we present a comprehensive survey on autoencoder and its various variants. Furthermore, we also present the lineage of the surveyed autoencoders. This paper can provide researchers engaged in related works with very valuable help.

关键词： autoencoder decoder deep learning feature learning generative model

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autoencoder-based method for online fault detection in discrete-event class Production Systems 17

Autoencoder-based method for online fault detection in discr...

引用

17th IFAC Workshop on discrete Event Systems (WODES)

作者： Saddem, Ramla Baptiste, Dylan Teingua, Ange Patrick Wabo Univ Reims CReSTIC Reims France

In the context of discrete-event systems (DES), the terms detection and diagnosis refer to two distinct stages of handling faults and anomalies. Both steps are critical for ensuring the reliable and safe operation of complex systems. In this paper, we propose the use of autoencoders for fault detection in an automated production system with sensors and actuators delivering discrete binary signals that can be modeled as DES. We train an autoencoder exclusively on data representing normal behavior. The model learns to encode typical patterns and reconstruct input data with low loss. A predetermined threshold, determined by the characteristics of the training data, is set for the reconstruction error. During normal behavior, the autoencoder is expected to achieve low reconstruction error below this threshold. When a fault occurs, the autoencoder strives to accurately reconstruct faulty data, leading to a higher error. The detection of a reconstruction error exceeding the threshold signals a potential fault in the system. The results of applying our method to the Factory IO software sorting system demonstrate the significant contribution and the interest of this method for detecting faults.

关键词： autoencoder Fault detection Automated Production System Deep Learning discrete-event systems.

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