检索结果-内蒙古大学图书馆

VAE-Based Latent Representations Learning for Botnet Detection in IoT Networks

JOURNAL OF NETWORK AND SYSTEMS MANAGEMENT 2023年第1期31卷 4-4页

作者： Snoussi, Ramzi Youssef, Habib Univ Sousse Prince Lab ISITCOM Hammam Sousse Sousse Tunisia

Botnets pose significant threats to cybersecurity. The infected Internet of Things (IoT) devices are used to launch unsupported malicious activities on target entities to disrupt their operations and services. To address this danger, we propose a machine learning-based method, for detecting botnets by analyzing network traffic data flow including various types of botnet attacks. Our method uses a hybrid model where a variational autoencoder (VAE) is trained in an unsupervised manner to learn latent representations that describe the benign traffic data, and one-class classifier (OCC) for detecting anomaly (also called novelty detection). The main aim of this research is to learn the discriminating representations of the normal data in low dimensional latent space generated by VAE, and thus improve the predictive power of the OCC to detect malicious traffic. We have evaluated the performance of our model, and compared it against baseline models using a real network based dataset, containing popular IoT devices, and presenting a wide variety of attacks from two recent botnet families Mirai and Bashlite. Tests showed that our model can detect botnets with a satisfactory performance.

关键词： Anomaly detection Botnet detection variational autoencoder One-class classification Latent representations learning

来源：评论

学校读者我要写书评

暂无评论

Anomaly detection in aeronautics data with quantum-compatible discrete deep generative model

引用

MACHINE LEARNING-SCIENCE AND TECHNOLOGY 2023年第3期4卷 035018页

作者： Templin, Thomas Memarzadeh, Milad Vinci, Walter Lott, P. Aaron Asanjan, Ata Akbari Armenakas, Anthony Alexiades Rieffel, Eleanor NASA Data Sci Grp Ames Res Ctr Moffett Field CA 94035 USA NASA Univ Space Res Assoc Data Sci Grp Ames Res Ctr Moffett Field CA 94035 USA HP SCDS Leon 24009 Spain NASA Univ Space Res Assoc Quantum Artificial Intelligence Lab Ames Res Ctr Moffett Field CA 94035 USA Harvard Univ Dept Phys Cambridge MA 02138 USA NASA Ames Res Ctr Quantum Artificial Intelligence Lab Moffett Field CA 94035 USA

Deep generative learning cannot only be used for generating new data with statistical characteristics derived from input data but also for anomaly detection, by separating nominal and anomalous instances based on their reconstruction quality. In this paper, we explore the performance of three unsupervised deep generative models-variational autoencoders (VAEs) with Gaussian, Bernoulli, and Boltzmann priors-in detecting anomalies in multivariate time series of commercial-flight operations. We created two VAE models with discrete latent variables (DVAEs), one with a factorized Bernoulli prior and one with a restricted Boltzmann machine (RBM) with novel positive-phase architecture as prior, because of the demand for discrete-variable models in machine-learning applications and because the integration of quantum devices based on two-level quantum systems requires such models. To the best of our knowledge, our work is the first that applies DVAE models to anomaly-detection tasks in the aerospace field. The DVAE with RBM prior, using a relatively simple-and classically or quantum-mechanically enhanceable-sampling technique for the evolution of the RBM's negative phase, performed better in detecting anomalies than the Bernoulli DVAE and on par with the Gaussian model, which has a continuous latent space. The transfer of a model to an unseen dataset with the same anomaly but without re-tuning of hyperparameters or re-training noticeably impaired anomaly-detection performance, but performance could be improved by post-training on the new dataset. The RBM model was robust to change of anomaly type and phase of flight during which the anomaly occurred. Our studies demonstrate the competitiveness of a discrete deep generative model with its Gaussian counterpart on anomaly-detection problems. Moreover, the DVAE model with RBM prior can be easily integrated with quantum sampling by outsourcing its generative process to measurements of quantum states obtained from a quantum anneale

关键词： generative modeling deep learning variational autoencoder anomaly detection restricted Boltzmann machine Gibbs sampling quantum-assisted machine learning

来源：评论

学校读者我要写书评

暂无评论

Unsupervised Cryo-EM Images Denoising and Clustering Based on Deep Convolutional autoencoder and K-Means plus

引用

IEEE TRANSACTIONS ON MEDICAL IMAGING 2023年第5期42卷 1509-1521页

作者： Zhang, Dongxu Yan, Yang Huang, Yulin Liu, Bowen Zheng, Qingbing Zhang, Jun Xia, Ningshao Xiamen Univ Sch Publ Hlth State Key Lab Mol Vaccinol & Mol Diagnost Xiamen 361102 Peoples R China Chinese Acad Med Sci Res Unit Frontier Technol Struct Vaccinol Xiamen 361102 Peoples R China

Cryo-electron microscopy (cryo-EM) is a widely used structural determination technique. Because of the extremely low signal-to-noise ratio (SNR) of images captured by cryo-EM, clustering single-particle cryo-EM images with high accuracy is challenging. To address this, we proposed an iterative denoising and clustering method based on a deep convolutional variational autoencoder and K-means++. The proposed method contains two modules: a denoising ResNet variational autoencoder (DRVAE), and Balance size K-means++ (BSK-means++). First, the DRVAE is trained in a fully unsupervised manner to initialize the neural network and obtain preliminary denoised images. Second, BSK-means++ is built for clustering denoised images, and images closer to class centers are divided into reliable samples. Third, the training of DRVAE is continued, while the class-average images are used as pseudo supervision of reliable samples to reserve more detailed information of denoised images. Finally, the second and third steps mentioned above can be performed jointly and iteratively until convergence occurs. The experimental results showed that the proposed method can generate reliable class average images and achieve better clustering accuracy and normalized mutual information than current methods. This study confirmed that DRVAE with BSK-means++ could achieve a good denoise performance on single-particle cryo-EM images, which can help researchers obtain information such as symmetry and heterogeneity of the target particles. In addition, the proposed method avoids the extreme imbalance of class size, which improves the reliability of the clustering result.

关键词： Training Reliability Noise reduction Image reconstruction Signal to noise ratio Clustering methods Neural networks Clustering image denoising K-means single-particle cryo-EM variational autoencoder

来源：评论

学校读者我要写书评

暂无评论

3D Generative Model Latent Disentanglement via Local Eigenprojection

引用

COMPUTER GRAPHICS FORUM 2023年第6期42卷 e14793-e14793页

作者： Foti, Simone Koo, Bongjin Stoyanov, Danail Clarkson, Matthew J. UCL London England Univ Calif Santa Barbara Santa Barbara CA USA

Designing realistic digital humans is extremely complex. Most data-driven generative models used to simplify the creation of their underlying geometric shape do not offer control over the generation of local shape attributes. In this paper, we overcome this limitation by introducing a novel loss function grounded in spectral geometry and applicable to different neural-network-based generative models of 3D head and body meshes. Encouraging the latent variables of mesh variational autoencoders (VAEs) or generative adversarial networks (GANs) to follow the local eigenprojections of identity attributes, we improve latent disentanglement and properly decouple the attribute creation. Experimental results show that our local eigenprojection disentangled (LED) models not only offer improved disentanglement with respect to the state-of-the-art, but also maintain good generation capabilities with training times comparable to the vanilla implementations of the models. Our code and pre-trained models are available at .

关键词： disentanglement generative adversarial networks geometric deep learning variational autoencoder

来源：评论

学校读者我要写书评

暂无评论

Deep generative model with time series-image encoding for manufacturing fault detection in die casting process

引用

JOURNAL OF INTELLIGENT MANUFACTURING 2023年第7期34卷 3001-3014页

作者： Song, Jiyoung Lee, Young Chul Lee, Jeongsu Korea Inst Ind Technol Smart Liquid Proc R&D Dept 113-58 Seohaean Ro Siheung Si Gyeonggi Do South Korea Gachon Univ Dept Mech Smart & Ind Engn 1342 Seongnamdaero Seongnam Si Gyeonggi Do South Korea

The increasing demand for advanced fault detection in manufacturing processes has encouraged the application of industrial intelligence based on deep learning. However, implementing deep learning technology at actual manufacturing sites remains challenging because the data acquired during the manufacturing process are not only unlabeled but also imbalanced time series data. In this study, we constructed semi-supervised manufacturing fault detection methods to deal with the imbalanced time series data obtained from manufacturing applications, based on recently proposed deep generative models: variational autoencoder-reconstruction along projection pathway (VAE-RaPP) and Fence generative adversarial network (Fence GAN). To apply a semi-supervised learning algorithm, 1000 labeled samples of good product were prepared. The deep generative models learned the features of good product from these labeled samples during training. Consequently, the model was sufficiently trained to distinguish good and defective product in unlabeled samples. Additionally, we converted the time series data acquired during the manufacturing process into images to improve the feature extraction capability of deep neural networks based on three encoding methods: Gramian angular difference field (GADF), Markov transition field (MTF), and recurrence plot (RP). The performance of these methods was then compared using four evaluation indicators: area under the receiver operating characteristic (AUROC), average precision (AP) score, precision-recall (PR) curve, and accuracy. The VAE-RaPP exhibited outstanding performance in all types of encoding methods when compared with the Fence GAN. This research provides a novel approach that combines the encoding of time series into images and deep generative models for manufacturing fault detection.

关键词： Fault detection Generative adversarial network variational autoencoder Time series data Image encoding Semi-supervised learning

来源：评论

学校读者我要写书评

暂无评论

CNN-VAE: An intelligent text representation algorithm

引用

JOURNAL OF SUPERCOMPUTING 2023年第11期79卷 12266-12291页

作者： Xu, Saijuan Guo, Canyang Zhu, Yuhan Liu, Genggeng Xiong, Neal Fujian Business Univ Coll Informat Engn Lianpan Rd 2 Fuzhou 350506 Fujian Peoples R China Fuzhou Univ Coll Comp & Data Sci Xueyuan Rd 2 Fuzhou 350116 Fujian Peoples R China Sul Ross State Univ Dept Comp Math & Phys Sci 1404 East Highway 90 Alpine TX 79830 USA

Collecting and analyzing data from all devices to improve the efficiency of business processes is an important task of Industrial Internet of Things (IIoT). In the age of data explosion, extensive text data generated by the IIoT have given birth to a variety of text representation methods. The task of text representation is to convert the natural language to a form that computer can understand with retaining the original semantics. However, these methods are difficult to effectively extract the semantic features among words and distinguish polysemy in natural language. Combining the advantages of convolutional neural network (CNN) and variational autoencoder (VAE), this paper proposes an intelligent CNN-VAE text representation algorithm as an advanced learning method for social big data within next-generation IIoT, which help users identify the information collected by sensors and perform further processing. This method employs the convolution layer to capture the local features of the context and uses the variational technique to reconstruct feature space to make it conform to the normal distribution. In addition, the improved word2vec model based on topical word embedding (TWE) is utilized to add topical information to word vectors to distinguish polysemy. This paper takes the social big data as an example to illustrate the way of the proposed algorithm applied in the next-generation IIoT and utilizes Cnews dataset to verify the performance of proposed method with four evaluating metrics (i.e., recall, accuracy, precision, and F1-score). Experimental results indicate that the proposed method outperforms word2vec-avg and CNN-AE in K-nearest neighbor (KNN), random forest (RF), and support vector machine (SVM) classifiers and distinguishes polysemy effectively.

关键词： Convolutional neural network Feature extraction Text representation Topical word embedding variational autoencoder

来源：评论

学校读者我要写书评

暂无评论

Machine Learning for Relaying Topology: Optimization of IoT Networks With Energy Harvesting

引用

IEEE ACCESS 2023年 11卷 41827-41839页

作者： Chung, Kiseop Lim, Jin-Taek Agcy Def Dev Daejeon 34186 South Korea

In this paper, we examine Internet of Things (IoT) systems related to smart cities, smart factories, connected cars, etc. To support such systems in a wide area with low power consumption, energy harvesting technology utilizing wireless charging infrastructure is necessary for the longevity of networks. Considering that the position and amount of energy charged for each device could be unbalanced according to the distribution of nodes and energy sources, maximizing the minimum throughput among all nodes has become an NP-hard challenging issue. To overcome this challenge, we propose a machine learning based relaying topology algorithm with a novel backward-pass rate assessment method to present proper learning direction and an iterative balancing time slot allocation algorithm which can utilize a node with sufficient energy as the relay. To validate our proposed scheme, we conducted simulations on our established system model;thus, we confirm that the proposed scheme is stable and superior to conventional schemes.

关键词： Topology Internet of Things Optimization Network topology Unsupervised learning Resource management Energy harvesting variational autoencoder IoT network TDMA system energy harvesting relay

来源：评论

学校读者我要写书评

暂无评论

A Deep Learning Based Induced GNSS Spoof Detection Framework

IEEE TRANSACTIONS ON MACHINE LEARNING IN COMMUNICATIONS AND ...

引用

IEEE TRANSACTIONS ON MACHINE LEARNING IN COMMUNICATIONS AND NETWORKING 2024年 2卷 457-478页

作者： Iqbal, Asif Aman, Muhammad Naveed Sikdar, Biplab Natl Univ Singapore Dept Elect & Comp Engn Singapore 117583 Singapore Univ Nebraska Lincoln Sch Comp Lincoln NE 68523 USA

The Global Navigation Satellite System (GNSS) plays a crucial role in critical infrastructure by delivering precise timing and positional data. Nonetheless, the civilian segment of the GNSS remains susceptible to various spoofing attacks, necessitating robust detection mechanisms. The ability to deter such attacks significantly enhances the reliability and security of systems utilizing GNSS technology. Supervised Machine Learning (ML) techniques have shown promise in spoof detection. However, their effectiveness hinges on training data encompassing all possible attack scenarios, rendering them vulnerable to novel attack vectors. To address this limitation, we explore representation learning-based methods. These methods can be trained with a single data class and subsequently applied to classify test samples as either belonging to the training class or not. In this context, we introduce a GNSS spoof detection model comprising a variational autoencoder (VAE) and a Generative Adversarial Network (GAN). The composite model is designed to efficiently learn the class distribution of the training data. The features used for training are extracted from the radio frequency and tracking modules of a standard GNSS receiver. To train our model, we leverage the Texas Spoofing Test Battery (TEXBAT) datasets. Our trained model yields three distinct detectors capable of effectively identifying spoofed signals. The detection performance across simpler to intermediate datasets for these detectors reaches approximately 99%, demonstrating their robustness. In the case of subtle attack scenario represented by DS-7, our approach achieves an approximate detection rate of 95%. In contrast, under supervised learning, the best detection score for DS-7 remains limited to 44.1%.

关键词： Global navigation satellite system Receivers Feature extraction Training Measurement Smart grids Monitoring Generative adversarial network GNSS security GPS spoofing receiver security representation learning spoof detection TEXBAT variational autoencoder

来源：评论

学校读者我要写书评

暂无评论

Deep generative learning for exploration in large electrochemical impedance dataset

引用

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2023年第PartC期126卷

作者： Doonyapisut, Dulyawat Kim, Byeongkyu Kim, Jung Kyu Lee, Eunseok Chung, Chan-Hwa Sungkyunkwan Univ Sch Chem Engn Suwon 16419 South Korea Sungkyunkwan Univ Coll Comp & Informat Suwon 16419 South Korea

The fields of energy storage, photocatalysis, and sensors have undergone substantial technological advancements, which have led to the generation of vast amounts of data on electrochemical impedance (EIS). The interpretation of large amounts of EIS data is a challenging task since the analysis of EIS data requires multiple steps to get a suitable equivalent circuit. Recently, some progress has been made in the machine learning (ML) model for EIS classification. However, most of the ML models are performed as a "black box" model, which provides only the classification result and lacks physical descriptor representation. Here, we apply variational autoencoders (VAE) to EIS data analysis, which includes classification, parameter prediction, and the visualization of physical descriptors. The VAE model performed well in the classification task, with an accuracy of 82.0%-92.4%. In the prediction task, VAE shows a high R-squared value on the Randles circuit. Additionally, the VAE model can map physical descriptors to the latent space, allowing the latent space to transform into a property space, which plays an important role in the optimization and exploration of novel materials research.

关键词： Electrochemical impedance spectroscopy Equivalent circuit model variational autoencoder Impedance optimization Impedance visualization

来源：评论

学校读者我要写书评

暂无评论

Unsupervised Transformer Balanced Hashing for Multispectral Remote Sensing Image Retrieval

引用

IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2023年 16卷 7089-7099页

作者： Chen, Yaxiong Wang, Fan Lu, Lin Xiong, Shengwu Wuhan Univ Technol Sch Comp Sci & Artificial Intelligence Wuhan 430070 Peoples R China Wuhan Univ Technol Shanghai Artificial Intelligence Lab Shanghai 200232 Peoples R China Wuhan Univ Technol Sanya Sci & Educ Innovat Park Sanya 572000 Peoples R China Wuhan Univ Technol Chongqing Res Inst Chongqing 401122 Peoples R China

For remote sensing (RS) image retrieval task, hashing technology have been extensively researched in recent works. Unsupervised hashing approaches have attracted much attention in the RS data processing field because label collection takes a lot of time. Most of which fail to consider the interactions among the multichannel information of multispectral RS images and the disparity between the hash-like codes space and the Hamming space, which lead to the poor performance of multispectral RS image retrieval. In this article, we tackle these dilemmas with a novel unsupervised hashing approach, namely Unsupervised Transformer Balanced Hashing (UTBH), to utilize a convolutional variational autoencoder architecture with a novel RS transformer to perform effective hash codes learning. We first integrate a convolutional variational autoencoder architecture with a novel RS transformer, which can guide the interactions among the multichannel information of multispectral RS images. Meanwhile, a new objective function is proposed to preserve discrimination of hash codes in the hashing learning process and reduce the disparity between the hash-like codes space and the Hamming space effectively. Finally, experimental results on two multispectral RS image datasets indicate that UTBH approach achieves superior performance over other unsupervised image retrieval approaches.

关键词： Hash codes multichannel information transformer variational autoencoder

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：