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An autoencoder-Based Task-Oriented Semantic Communication System for M2M Communication

ALGORITHMS 2024年第11期17卷 492-492页

作者： Samarathunga, Prabhath Rezaei, Hossein Lokumarambage, Maheshi Sivalingam, Thushan Rajatheva, Nandana Fernando, Anil Univ Strathclyde Dept Comp & Informat Sci Glasgow City G1 1XQ Scotland Univ Oulu Ctr Wireless Commun Oulu 90570 Finland

Semantic communication (SC) is a communication paradigm that has gained significant attention, as it offers a potential solution to move beyond Shannon's formulation in bandwidth-limited communication channels by delivering the semantic meaning of the message rather than its exact form. In this paper, we propose an autoencoder-based SC system for transmitting images between two machines over error-prone channels to support emerging applications such as VIoT, XR, M2M, and M2H communications. The proposed autoencoder architecture, with a semantically modeled encoder and decoder, transmits image data as a reduced-dimension vector (latent vector) through an error-prone channel. The decoder then reconstructs the image to determine its M2M implications. The autoencoder is trained for different noise levels under various channel conditions, and both image quality and classification accuracy are used to evaluate the system's efficacy. A CNN image classifier measures accuracy, as no image quality metric is available for SC yet. The simulation results show that all proposed autoencoders maintain high image quality and classification accuracy at high SNRs, while the autoencoder trained with zero noise underperforms other trained autoencoders at moderate SNRs. The results further indicate that all other proposed autoencoders trained under different noise levels are highly robust against channel impairments. We compare the proposed system against a comparable JPEG transmission system, and results reveal that the proposed system outperforms the JPEG system in compression efficiency by up to 50% and in received image quality with an image coding gain of up to 17 dB.

关键词： autoencoder error-correcting codes M2M communication polar codes semantic coding semantic communication successive cancellation decoding

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Evaluation of HTTP request anomaly detection model using fastText and convolutional autoencoder

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IEICE COMMUNICATIONS EXPRESS 2024年第7期13卷 240-243页

作者： Yamada, Haruta Kawahara, Ryoichi Toyo Univ Fac Informat Networking Innovat & Design Tokyo 1158650 Japan

With the advent of the Internet and its close connection to people's lives, web applications have become increasingly important. To ensure that the web application is secure, a web application firewall (WAF) detects and stops attacks that exploit application vulnerabilities in communication with server applications. However, these firewalls require continuous tuning by experts with in-depth knowledge of the technologies and services provided, which may become a major obstacle to the introduction of WAF. To resolve this problem, we developed two autoencoder-based models based on an unsupervised learning model that uses only normal requests, considering the implementation and operation costs. We then evaluated the performance of the two autoencoder-based models. The first model converts a hypertext transfer protocol (HTTP) request into ASCII codes and learns their relationship in a normal request using an autoencoder. The second model generates an array of word vectors using fastText and learns using a convolutional autoencoder, which solves the problem identified in the performance evaluation of the first model where the problem was that the simple conversion to ASCII codes was not enough to distinguish between normal and anomalous requests. The two models were evaluated using the HTTP DATASET CSIC2010 dataset. The AUC for the second model was approximately 0.94 while that for the first model was approximately 0.71. This means that the second model has higher accuracy despite being an unsupervised approach, one that does not require labeled anomalous requests, and can be applied with low costs.

关键词： web application firewall anomaly detection autoencoder fast Text

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autoencoder combined with the multilayer perceptron for Alzheimer's disease classification

Autoencoder combined with the multilayer perceptron for Alzh...

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作者： Xinran Yu Department of Biochemistry and Molecular Biology University of California

Alzheimer's disease(AD) is a prevalent neurodegenerative disorder that poses significant challenges for accurate diagnosis and *** classification of AD Neurofibrillary Changes(ADNC) levels is crucial for understanding disease progression and developing effective *** this paper,a method was proposed for classifying ADNC levels based on single-cell RNA sequencing(scRNA-seq) data obtained from the SEA-AD *** autoencoder was employed to reduce the dimensionality of the scRNA-seq data,followed by a Multilayer Perceptron(MLP) for classification based on the autoencoder's *** autoencoder effectively reduces the dimension of the scRNA-seq data from4344 to 30 ***,the embedding does not exhibit clear boundaries between different ADNC *** MLP model achieves a classification accuracy of 39% on the ADNC levels,indicating the complexity of the task and the need for more advanced classification ***,the overfitting in both models was observed,and dropout regularization is applied to mitigate this *** the results indicate the potential of feature extraction and dimensionality reduction using autoencoders,the accuracy of ADNC level classification remains *** multiple approaches and aspects in AD diagnosis is necessary,as RNA-seq data alone may not be sufficient for accurate *** work could explore more sophisticated classification algorithms to improve the accuracy of ADNC level classification and consider integrating other data modalities to enhance disease diagnosis and understanding.

关键词： Alzheimer's disease autoencoder machine learning

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An unsupervised fault monitoring framework for blast furnace: Information extraction enhanced GRU-GMM-autoencoder

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JOURNAL OF PROCESS CONTROL 2023年 130卷

作者： Zhu, Xiongzhuo Yang, Chunjie Yang, Chong Gao, Dali Lou, Siwei Zhejiang Univ Coll Control Sci & Engn Hangzhou 310027 Peoples R China

Fault monitoring of the blast furnace ironmaking process (BFIP) is challenging due to the nonlinearity and dynamic characteristics. The lack of fault labels and the existence of outliers further increase the difficulty of fault monitoring. In particular, these outliers are considered normal operations in BFIP. In this case, an unsupervised autoencoder (AE)-based monitoring framework is proposed. Firstly, the gate recurrent unit (GRU) is used as an encoder to adaptively capture the nonlinear dynamic features of data. The extracted features are proved to be directly related to monitoring. Second, the variance constraint of latent variables (LVs) is added to the loss function to enhance the ability of information extraction. Third, an approach that combines AE structure and GMM to deal with outliers is proposed. Based on the probability clustering results of GMM, monitoring thresholds can be calculated adaptively for different samples. Meanwhile, the fault variables can also be located according to the increment of reconstruction error of GRU-AE. Finally, the proposed method is validated by a numerical example and the practical data gathered from the BF of a Chinese steel group. Compared to unsupervised statistical methods and deep learning methods, the proposed algorithm achieves the lowest false alarm rate (FAR) + missing alarm rate (MAR).

关键词： Blast furnace Gated recurrent unit autoencoder Process monitoring Gaussian mixture model

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SCADefender: An autoencoder-Based Defense for CNN-Based Image Classifiers

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INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE 2023年第12期37卷 2350027-2350027页

作者： Nguyen, Duc-Anh Do Minh, Kha Nhu, Ngoc Nguyen Hung, Pham Ngoc VNU Univ Engn & Technol Dept Software Engn 144 Xuanthuy St Hanoi Vietnam

Convolutional neural networks (CNNs) have been enormously successful in a variety of image recognition tasks. Robustness is an important metric to evaluate the quality of CNNs. However, recent research shows that CNNs are particularly vulnerable to adversarial attacks. This paper proposes an adversarial defense method to increase the robustness of CNNs, namely, SCADefender. The proposed method trains a reformer on adversarial examples and the training set of a target classifier. The architecture of the reformer is stacked convolutional autoencoder. The adversarial examples are generated by using various adversarial attacks such as untargeted FGSM, untargeted CW L2 and untargeted BIS. Given an input image, the trained reformer could remove the adversarial perturbations with a low computational cost. To demonstrate the effectiveness, the proposed method is compared with PuVAE, MagNet, and adversarial training on three well-known datasets including MNIST, Fashion-MNIST, and CIFAR-10. In terms of the average detection rate, the proposed method outperforms other methods. While the proposed method achieves an average detection rate of 97.78% for MNIST, 90.43% for Fashion-MNIST, and 80.64% for CIFAR-10, the comparable methods achieve only 23.69- 86.18% for MNIST, 63.90-79.70% for Fashion-MNIST, and 25.55-77.36% for CIFAR-10.

关键词： Adversarial defense adversarial example robustness autoencoder white-box attack

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STEAMCODER: Spatial and Temporal Adaptive Dynamic Convolution autoencoder for Anomaly Detection

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KNOWLEDGE-BASED SYSTEMS 2023年第1期279卷

作者： Xu, Peng Gan, Honglu Fu, Hua Zhang, Zhongbao Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing Peoples R China

The anomaly detection algorithm greatly improves the reliability of equipment operation. Traditional anomaly detection algorithms are mostly designed for large data sets, making it difficult to detect anomalies when where is not enough accumulated equipment data. Therefore, detecting anomalies during the cold-start stage is also challenging. In industrial production, the working conditions of the equipment are constantly adjusted, and the amount or characteristics of data for training also change frequently. Hence, an adaptive algorithm is required to address these problems. We design a novel algorithm called Spatial-TEmporal Adaptive dynaMic Convolution autoencoder for Anomaly Detection (STEAMCODER), specifically. This algorithm first converts the data into a spatial-temporal anomaly feature matrix and then utilizes a dynamic convolution autoencoder to analyze the matrix and detect anomalies. Finally, we conduct extensive experiments to validate the performance of STEAMCODER and the results demonstrated its superiority compared to state-of-the-art algorithms in adaptive anomaly detection, including F1 score and other indicators. Furthermore, STEAMCODER is capable of filtering false positives caused by glitch data, enabling the early detection of equipment anomalies. (c) 2023 Elsevier B.V. All rights reserved.

关键词： Anomaly detection Dynamic CNN autoencoder

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Accelerator tuning method using autoencoder and Bayesian optimization

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NUCLEAR INSTRUMENTS & METHODS IN PHYSICS RESEARCH SECTION A-ACCELERATORS SPECTROMETERS DETECTORS AND ASSOCIATED EQUIPMENT 2023年 1057卷

作者： Morita, Yasuyuki Washio, Takashi Nakashima, Yuta RIKEN Nishina Ctr Accelerator Based Sci 2-1 Hirosawa Wako Saitama 3510198 Japan Osaka Univ Inst Sci & Ind Res 8-1 Mihogaoka Ibaraki Osaka 5670047 Japan Osaka Univ Inst Databil Sci Technoalliance bldg C5032-8 Yamadaoka Suita Osaka 5650871 Japan

During beam-accelerator operation, a large number of parameters need to be tuned. In recent years, tuning methods based on machine learning have been extensively studied. Bayesian optimization (BO) has attracted considerable attention as an excellent method for accelerator tuning. However, its applicability is limited by the number of parameters that can be tuned. In this study, we propose an optimization method that combines autoencoder and BO to tune a large number of parameters. We verified it using beam transport simulations. We confirmed a higher tuning effect in a shorter time than when using only BO. The proposed method is expected to speed up the accelerator operation and provide comprehensive tuning.

关键词： Accelerator Machine learning Bayesian optimization autoencoder

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A Novel Fault Detection Method Based on One-Dimension Convolutional Adversarial autoencoder (1DAAE)

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PROCESSES 2023年第2期11卷 384页

作者： Wang, Jian Li, Yakun Han, Zhiyan Bohai Univ Sch Control Sci & Engn Jinzhou 121013 Peoples R China

Fault detection is an important and demanding problem in industry. Recently, many researchers have addressed the use of deep learning architectures for fault detection applications such as an autoencoder. Traditional methods based on an autoencoder usually complete fault detection by comparing reconstruction errors, and ignore a lot of useful information about the distribution of latent variables. To deal with this problem, this paper proposes a novel unsupervised fault detection method named one-dimension convolutional adversarial autoencoder (1DAAE), which introduces two new ideas: one-dimension convolution layers for the encoder to obtain better features and the adversarial thought to impose the latent variable z to cluster into a prior distribution. The proposed method not only has powerful feature representation ability than the traditional autoencoder, but has also enhanced the discrimination ability by imposing a prior distribution of the latent variables to cluster. Then, two anomaly scores for 1DAAE were proposed to detect fault samples, one based on reconstruction errors, and the other based on latent variable distribution. Finally, it was shown by the experiments that the proposed method outperformed the autoencoder-based, adversarial autoencoder-based, one-dimension convolutional autoencoder-based and generative adversarial network-based algorithms on the Tennessee Eastman process. Through the experiments, we found that the both one-dimension convolution layers and the latent vector distribution are helpful for fault detection.

关键词： fault detection autoencoder convolutional layer Tennessee Eastman process unsupervised learning

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Beyond vanilla: Improved autoencoder-based ensemble in-vehicle intrusion detection system

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JOURNAL OF INFORMATION SECURITY AND APPLICATIONS 2023年 77卷

作者： Rajapaksha, Sampath Kalutarage, Harsha Al-Kadri, M. Omar Petrovski, Andrei Madzudzo, Garikayi Robert Gordon Univ Aberdeen Scotland Univ Doha Sci & Technol Doha Qatar Horiba Mira Ltd Nuneaton England

Modern automobiles are equipped with a large number of electronic control units (ECUs) to provide safe, driver assistance and comfortable services. The controller area network (CAN) provides near real-time data transmission between ECUs with adequate reliability for in-vehicle communication. However, the lack of security measures such as authentication and encryption makes the CAN bus vulnerable to cyberattacks, which affect the safety of passengers and the surrounding environment. Detecting attacks on the CAN bus, particularly masquerade attacks, presents significant challenges. It necessitates an intrusion detection system (IDS) that effectively utilizes both CAN ID and payload data to ensure thorough detection and protection against a wide range of attacks, all while operating within the constraints of limited computing resources. This paper introduces an ensemble IDS that combines a gated recurrent unit (GRU) network and a novel autoencoder (AE) model to identify cyberattacks on the CAN bus. AEs are expected to produce higher reconstruction errors for anomalous inputs, making them suitable for anomaly detection. However, vanilla AE models often suffer from overgeneralization, reconstructing anomalies without significant errors, resulting in many false negatives. To address this issue, this paper proposes a novel AE called Latent AE, which incorporates a shallow AE into the latent space. The Latent AE model utilizes Cramer's statistic-based feature selection technique and a transformed CAN payload data structure to enhance its efficiency. The proposed ensemble IDS enhances attack detection capabilities by leveraging the best capabilities of independent GRU and Latent AE models, while mitigating the weaknesses associated with each individual model. The evaluation of the IDS on two public datasets, encompassing 13 different attacks, including sophisticated masquerade attacks, demonstrates its superiority over baseline models with near real-time detection latency of

关键词： Controller Area Network (CAN) Machine learning Automotive cybersecurity Deep learning autoencoder Anomaly detection

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Intelligent Differentiation Framework for Lewy Body Dementia and Alzheimer's disease using Adaptive Multi-Cascaded ResNet-autoencoder-LSTM Network

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INTERNATIONAL JOURNAL OF IMAGE AND GRAPHICS 2024年第0期

作者： Sravani, K. RaviSankar, V. GITAM Univ Dept Comp Sci & Engn Hyderabad 502329 India

In recent years, most of the patients with dementia have acquired healthcare systems within the primary care system and they also have some challenging psychiatric and medical issues. Here, dementia-based symptoms are not identified in the primary care center, because they are affected by various factors like psychological symptoms, clinically relevant behavior, numerous psychotropic medications, and multiple chronic medical conditions. To enhance the healthcare-related applications, the primary healthcare system with additional resources like coordination with interdisciplinary dementia specialists, feasible diagnosis, and screening process need to be improved. Therefore, the differentiation between Alzheimer's Disease (AD) and Lewy Body Dementia (LBD) has been acquired to provide the best clinical support to the patients. In this research work, the deep structure depending on AD and LBD systems has been implemented with the help of an adaptive algorithm to provide promising outcomes over dementia detection. Initially, the input images are collected from online sources. Thus, the collected images are forwarded to the newly designed Multi-Cascaded Deep Learning (MSDL), where the ResNet, autoencoder, and weighted Long-Short Term Memory (LSTM) networks are serially cascaded to provide effective classification results. Then, the fully connected layer of ResNet is given to the autoencoder structure. Here, the output from the encoder phase is optimized by using the Adaptive Water Wave Cuttlefish Optimization (AWWCO), which is derived from the Water Wave Optimization (WWO) and Cuttlefish Algorithm (CA), and the resultant selected output is fed to the weight-optimized LSTM network. Further, the parameters in the MSDL network are optimized by using the same AWWCO algorithm. Finally, the performance comparison over different heuristic algorithms and conventional dementia detection approaches is done for the validation of the overall effectiveness of the suggested model in te

关键词： Disease differentiation framework Alzheimer's disease Lewy body dementia multi-cascaded deep learning adaptive water wave cuttlefish optimization weighted long-short term memory ResNet autoencoder

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