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6th International Conference on AI Circuits and Systems (AICAS)

作者： Enttsel, Andriy Marchioni, Alex Setti, Gianluca Mangia, Mauro Rovatti, Riccardo Univ Bologna DEI Bologna Italy Univ Bologna ARCES Bologna Italy KAUST CEMSE Thuwal Saudi Arabia

ISBN: (纸本)9798350383638;9798350383645

Monitoring systems produce and transmit large amounts of data. For an efficient transmission, data is often compressed and autoencoders are a widely adopted neural network-based solution. However, this processing step leads to a loss of information that may negatively impact the performance of downstream tasks, such as anomaly detection. In this work, we propose a loss function for an autoencoder that addresses both compression and anomaly detection. Our key contribution is the inclusion of a regularization term based on information-theoretic quantities that characterize an anomaly detector processing compressed signals. As a result, the proposed approach allows for a better use of the communication channel such that the information preserved by the compressed signal is optimized for both detection and reconstruction, even in scenarios with lightweight compression. We tested the proposed technique with ECG signals affected by synthetic anomalies and the experiments demonstrated an average 17% increase in the probability of detection across three standard detectors. Additionally, we proved that our approach is generalizable to image data.

关键词： anomaly detection dimensionality reduction autoencoder information theory compression

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Anomaly Detection-Reconstruction Trade-off in autoencoder-based Compression 32

Anomaly Detection-Reconstruction Trade-off in Autoencoder-ba...

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32nd European Signal Processing Conference (EUSIPCO)

作者： Enttsel, Andriy Marchioni, Alex Manovi, Livia Nikpali, Riccardo Ravaglia, Gabriele Setti, Gianluca Rovatti, Riccardo Mangia, Mauro Univ Bologna DEI Bologna Italy Univ Bologna ARCES Bologna Italy KAUST CEMSE Thuwal Saudi Arabia

ISBN: (纸本)9789464593617;9798331519773

Monitoring systems generate and transmit large volumes of data to facilities capable of effectively performing multiple tasks. Data is often compressed and autoencoders have emerged as a promising neural network-based approach. This work focuses on a scenario in which the receiver performs reconstruction and anomaly detection tasks. We examine how two autoencoder-based compression strategies administer the trade-off between reconstruction and anomaly detection. The experiments consider two scenarios: ECG time series and CIFAR-10 images. Each dataset is corrupted by five anomalies with different intensities and assessed with two detectors. We highlight the pros and cons of the two approaches showing that that their efficacy depends on a specific anomaly and setting.

关键词： anomaly detection dimensionality reduction autoencoder information theory compression

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SMAE: A Split Masked Graph autoencoder

SMAE: A Split Masked Graph Autoencoder

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Wang, Rutting Zuo, Enguang Chen, Chen Chen, Cheng Yan, Junyi Zhong, Jie Yan, Ziwei Lv, Xiaoyi Xinjiang Univ Coll Software Urumqi Xinjiang Peoples R China Xinjiang Univ Coll Intelligent Sci & Technol Urumqi Xinjiang Peoples R China

ISBN: (纸本)9798350390155;9798350390162

autoencoders, as a generative self-supervised learning, have received more and more attention in recent years in image, video, and other media-related information processing. However, Graph autoencoder (GAE) has yet to achieve the capability demonstrated by contrastive learning in the taskcentered on attribute networks. The main limitation lies in the fact that traditional autoencoder architectures require pretext tasks that align with downstream tasks, resulting in limited expressive power of the encoder. In this paper, we propose a novel separable-task generative self-supervised learning framework capable of providing high-quality representations, Split Masked autoencoder (SMAE), which unleashes the encoder's ability to extract representations through an intelligent design. Our approach focuses on unlocking the potential of the encoder by introducing encoding transfer and feature replacement strategies, thereby enabling self-supervised pretext tasks to achieve atomic separation and fully unleash the encoder's feature representation potential. We conducted extensive experiments on widely-used graph classification datasets, and the results demonstrate that SMAE outperforms state-of-the-art baselines in terms of graph classification accuracy and generation quality. Furthermore, our experimental findings show that prediction at the representation layer is more effective than original graph layer reconstruction in the field of masked graph autoencoders.

关键词： autoencoder attribute network graph representation learning masked graph

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Boarding for ISS: Imbalanced Self-Supervised Discovery of a Scaled autoencoder for Mixed Tabular Datasets

Boarding for ISS: Imbalanced Self-Supervised Discovery of a ...

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International Joint Conference on Neural Networks (IJCNN)

作者： Stocksieker, Samuel Pommeret, Denys Charpentier, Arthur Univ Claude Bernard Lyon 1 UR SAF Lyon France Aix Marseille Univ CNRS Cent Marseille I2M Marseille France Univ Quebec Montreal Dept Math Montreal PQ Canada

ISBN: (纸本)9798350359329;9798350359312

The field of imbalanced self-supervised learning, especially in the context of tabular data, has not been extensively studied. Existing research has predominantly focused on image datasets. This paper aims to fill this gap by examining the specific challenges posed by data imbalance in self-supervised learning in the domain of tabular data, with a primary focus on autoencoders. autoencoders are widely employed for learning and constructing a new representation of a dataset, particularly for dimensionality reduction. They are also often used for generative model learning, as seen in variational autoencoders. When dealing with mixed tabular data, qualitative variables are often encoded using a one-hot encoder with a standard loss function (MSE or Cross Entropy). In this paper, we analyze the drawbacks of this approach, especially when categorical variables are imbalanced. We propose a novel metric to balance learning: a Multi-Supervised Balanced MSE. This approach reduces the reconstruction error by balancing the influence of variables. Finally, we empirically demonstrate that this new metric, compared to the standard MSE: i) outperforms when the dataset is imbalanced, especially when the learning process is insufficient, and ii) provides similar results in the opposite case.

关键词： autoencoder Imbalanced Mixed Tabular Data Self-Supervised Learning

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An effective GPU-based random grid secret sharing using an autoencoder image super-resolution

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COGENT ENGINEERING 2024年第1期11卷

作者： Holla, M. Raviraja Suma, D. Manipal Acad Higher Educ MAHE Manipal Inst Technol Dept Informat & Commun Technol Manipal India

Visual crypto-system is a class of cryptography intended to secure images. Random-grid crypto-system is a type of visual cryptosystem that generates an encrypted grid of the secret image utilizing a pre-encoded grid and the secret image. The random grid research is still engaging in three dimensions: security, quality, and efficiency. Though there are many works in improving security, there is scope for investigation in the other two directions from the perspective of technological advancements. There has been a significant increase in the number of Graphical Processing Unit (GPU) cores for which the random grid models are intuitively amenable. The random grid secret sharing models demand more improvement in the quality of the reconstructed image as they achieved only 50% contrast. In this paper, we proposed a GPU based random-grid model to improve its efficiency by exploiting the data-parallelism inherent in the model. In addition to this speedup of 3151x, we restored the secret image with a quality almost equal to the original secret image using autoencoder super-resolution. Objective quality measures such as MSE, NCC, NAE and SSIM for the proposed model empirically confirm the improvement in image quality compared to other state-of-the-art models.

关键词： Visual secret sharing GPU halftoning autoencoder super-resolution Computing & IT Security Computer Science (General) Computer Engineering

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Assessment of Water Conservation Function Based on Deep autoencoder 15

Assessment of Water Conservation Function Based on Deep Auto...

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15th Ubiquitous Computing Electronics and Mobile Communication Conference

作者： Fan, Xiaoqing Lin, Xianghong Liu, Hao Northwest Normal Univ Coll Comp Sci & Engn Lanzhou Peoples R China Ningxia Univ Coll Agr Yinchuan Ningxia Peoples R China

ISBN: (纸本)9798331540913;9798331540906

The accuracy of water conservation assessments is crucial for formulating water resource management and ecological protection policies. However, existing methods overly rely on expert judgment and struggle with precision when handling high-dimensional data. To address this, we propose a deep autoencoder-based method for evaluating water conservation functions. For the Gannan water conservation area, we developed a comprehensive evaluation index system and integrated multi-source data to create an ecological dataset. By employing a deep autoencoder model combined with convolutional neural networks and a joint training strategy, we achieved feature extraction and dimensionality reduction, mapping high-dimensional data to a low-dimensional latent space. Subsequently, we used the K-means clustering algorithm to validate the model's classification performance. The clustering accuracy and FMI based on AE-CNN extracted latent variables were significantly higher than those obtained by clustering the raw data directly. This demonstrates that the model effectively extracts data features and significantly enhances classification accuracy, providing robust support for ecological protection and water management.

关键词： water conservation assessment autoencoder evaluation index system multi-source heterogeneous data convolutional neural network joint training

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TAElog: A Novel Transformer autoencoder-Based Log Anomaly Detection Method 19th

TAElog: A Novel Transformer AutoEncoder-Based Log Anomaly De...

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19th International Conference on Information Security and Cryptology (Inscrypt)

作者： Zhao, Changzhi Huang, Kezhen Wu, Di Han, Xueying Du, Dan Zhou, Yutian Lu, Zhigang Liu, Yuling Chinese Acad Sci Inst Informat Engn Beijing 10085 Peoples R China Univ Chinese Acad Sci Sch Cyber Secur Beijing 100049 Peoples R China Chinese Acad Sci Inst Software Beijing 100190 Peoples R China China Cybersecur Review Technol & Certificat Ctr Beijing Peoples R China Fudan Univ Sch Data Sci Shanghai 200433 Peoples R China

ISBN: (纸本)9789819709441;9789819709458

Log anomaly detection serves as an effective approach for identifying threats. autoencoder-based detection methods address positive and negative sample imbalance issues and have been extensively adopted in practical applications. However, most existing methods necessitate a sliding window to adapt to the autoencoder's base network, leading to information confusion and diminished resilience. Furthermore, detection results may be worthless when a single log comprises numerous unbalanced log records. In response, we propose TAElog, a novel framework employing a transformer-based autoencoder designed to extract precise information from logs without the need for sliding windows. TAElog also incorporates a new loss calculation that computes both high-dimensional metrics and divergence information, enhancing detection performance in intricate situations with diverse and unbalanced log records. Moreover, our framework covers preprocessing to increase the compatibility between text and numeric logs. To verify the effectiveness of TAElog, we evaluate its performance against other methods on both textual and numerical logs. Additionally, we assess various preprocessing and loss computation approaches to determine the optimal configuration within our method. Experimental results demonstrate that TAElog not only achieves superior accuracy rates but also boasts increased processing speed.

关键词： Self-supervised learning Log anomaly detection autoencoder

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Enhanced Denoising autoencoder-aided Bad Data Filtering for Synchrophasor-based State Estimation

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CSEE Journal of Power and Energy Systems 2022年第2期8卷 640-651页

作者： Guanyu Tian Yingzhong Gu Zhe Yu Qibing Zhang Di Shi Qun Zhou Zhiwei Wang GEIRI North America San JoseCA 95134 USA GEIRI North America San JoseCA 95134 USA on leave from Department of Electrical and Computer EngineeringUniversity of Central FloridaOrlandoFL 32816 USA Grid Dispatch Center State Grid Jiangsu Electric Power Company Ltd.Nanjing 210024China Department of Electrical and Computer Engineering University of Central FloridaOrlandoFL 32816 USA

Due to its high accuracy and ease of calculation,synchrophasor-based linear state estimation(LSE)has attracted a lot of attention in the last decade and has formed the cornerstone of many wide area monitor system(WAMS)***,an increasing number of data quality concerns have been reported,among which bad data can significantly undermine the performance of LSE and many other WAMS applications it *** data filtering can be difficult in practice due to a variety of issues such as limited processing time,non-uniform and changing patterns,and *** pre-process phasor measurement unit(PMU)measurements for LSE,we propose an improved denoising autoencoder(DA)-aided bad data filtering strategy in this *** data is first identified by the classifier module of the proposed DA and then recovered by the autoencoder *** characteristics distinguish the proposed methodology:1)The approach is lightweight and can be implemented at individual PMU level to achieve maximum parallelism and high efficiency,making it suited for real-time processing;2)the system not only identifies bad data but also recovers it,especially for critical *** use numerical experiments employing both simulated and real-world phasor data to validate and illustrate the effectiveness of the proposed method.

关键词： autoencoder bad data processing linear state estimation PMU

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Game-Theoretic Lightweight autoencoder Design for Intrusion Detection 25

Game-Theoretic Lightweight Autoencoder Design for Intrusion ...

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IEEE Wireless Communications and Networking Conference (IEEE WCNC)

作者： Rheey, Joohong Park, Hyunggon Ewha Womans Univ Dept Elect & Elect Engn Grad Program Smart Factory Seoul South Korea

ISBN: (纸本)9798350303582;9798350303599

In response to the security threats in wireless networks with concurrent device connections, deploying Intrusion Detection Systems (IDS) at the network edge is a promising strategy. However, this approach must take into account the resource constraints incurred by power-limited edge devices, requiring a lightweight solution to IDS. At the same time, the lightweight IDS solution has to minimize performance degradation as higher detection performance is also a key requirement of IDS. In this paper, we design a lightweight autoencoder with explainability, employing the Shapley value to measure unit importance and link importance. This approach can selectively activate only critical components, thereby reducing the complexity for IDS while effectively lowering its performance degradation. We confirm that the proposed algorithm is robust against the harsh sparsity of the autoencoder. Moreover, the sparsity of the proposed lightweight autoencoder can be easily manageable, such that it can be controlled to satisfy the potential constraints of power-limited edge devices. Therefore, the solution is a suitable algorithm for IDS that can be deployed on edge devices in wireless networks.

关键词： Lightweight intrusion detection autoencoder Shapley value

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CALLM: Cascading autoencoder and Large Language Model for Video Anomaly Detection 13

CALLM: Cascading Autoencoder and Large Language Model for Vi...

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13th International Conference on Image Processing Theory Tools and Applications

作者： Ntelopoulos, Apostolos Nasrollahi, Kamal Aalborg Univ Dept Elect Syst Aalborg Denmark Aalborg Univ Visual Anal & Percept Lab Copenhagen Denmark Milestone Syst Copenhagen Denmark

ISBN: (纸本)9798331541859;9798331541842

This paper introduces a new approach using a 3D Deep autoencoder and a Large Visual Language Model (LVLM) to bridge the gap between video data and multi-modal models for Video Anomaly Detection. The study explores the limitations of previous architectures, particularly their lack of expertise when encountering out-of-distribution instances. By integrating an autoencoder and an LVLM in the same pipeline, this method predicts an abnormality's presence and provides a detailed explanation. Moreover, this can be achieved by employing binary classification and automatically prompting a new query. Testing reveals that the inference capability of the system offers a promising solution to the shortcomings of industrial models. However, the lack of high-quality instruction-follow video data for anomaly detection necessitates a weakly supervised method. Current limitations from the LLM domain, such as object hallucination and low physics perception, are acknowledged, highlighting the need for further research to improve model design and data quality for the video anomaly detection domain.

关键词： cascade video anomaly detection autoencoder multimodal LLM

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