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23rd Asia-Pacific Network Operations and Management Symposium (APNOMS)

作者： Wu, Jihua Zhang, Lei Liu, Cong Qi, Qi Wang, Jingyu Xu, Tong Liao, Jianxin Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing Peoples R China China United Telecommun Co Ltd Beijing Peoples R China China Mobile Res Inst Beijing Peoples R China

ISBN: (数字)9784885523397

ISBN: (纸本)9784885523397

Network anomaly detection provides a reliable and stable service to detect faults and prevent security attacks effectively. However, existing detection methods still encounter many challenges. The supervised learning method is unsuitable because the anomaly samples are seriously sparse and hard to label. Unsupervised learning, as a promising method, is widely used while the discriminative features are ignored when reconstructing from the normal feature space. This paper proposes a novel probability correlation learning based on autoencoder called PCDetect, a semi-supervised learning method. Since we assumed the anomaly samples deviate from the distribution of normal samples, approximating the distribution of original data is proposed as an efficient preprocessing methodology to capture the discriminative features. Moreover, an encoder-decoder neural network associated with the proposed loss function is designed to learn the low-dimensional feature representation from raw data and constrain the latent representation to follow different referenced distributions based on a few anomaly labels. In this way, The correlation of the referenced distribution and the reconstruction of latent representation will be used to quantify the probability of anomaly. Extensive experiments are conducted on two public real-world datasets, NSL-KDD and UNSW-NB15. Results show the proposed PCDetect can efficiently cope with the imbalance and high-dimensional issues compared with several popular supervised learning methods, significantly improve the accuracy, and reduce the false rate as a whole compared with unsupervised learning.

关键词： anomaly detection autoencoder expectation maximization class imbalance

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Autonomous driving system with feature extraction using a binarized autoencoder 21

Autonomous driving system with feature extraction using a bi...

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21st International Conference on Field-Programmable Technology (ICFPT)

作者： Hisafuru, Kota Negishi, Ryotaro Kawakami, Soma Sato, Dai Yamashita, Kazuki Fukada, Keisuke Togawa, Nozomu Waseda Univ Dept Comp Sci & Commun Engn Tokyo Japan

ISBN: (数字)9781665453363

ISBN: (纸本)9781665453363

In this study, we present an autonomous driving system that utilizes a binarized autoencoder implemented on a Field Programmable Gate Array (FPGA). The binarized autoencoder compresses the image into optimal features in this system. The recurrent neural network then determines the following control based on the feature values extracted from the autoencoder and the rotation speed of the motor. We reduced the model size by binarizing the autoencoder because of the limited on-chip memory of the FPGA. We implemented the system on an Ultra96V2, a board with a programmable logic and processing system. The robot employing our implemented system exhibits robust control by recognizing the entire road marking and road edge line as a feature and drives autonomously along the specified route.

关键词： autonomous driving FPGA autoencoder binarized neural network

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Convolution autoencoder based Classification Model for Content Based Image Retrieval

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Procedia Computer Science 2025年 258卷 1865-1876页

作者： Rishi Rai Dilip Singh Sisodia

The Content Based Image Retrieval (CBIR) has gained significant importance due to surge of online images and its diverse applications. The CBIR system gets an image as input and returns the most analogous and pertinent image from the database. The CBIR has a wide variety of applications ranging from crime prevention to medical diagnosis and an efficient CBIR system may further improve these areas. In CBIR systems images are converted into binary codes for search and retrieval. There are many approaches for converting images into binary codes but most of them could not properly represent the non-linear relationship exist within the images. To overcome this issue many deep learning architectures and models have been proposed for learning the non-linear relationship within an image. The models used may be unsupervised learning models such as autoencoders or supervised learning models like Convolutional Neural Networks (CNN) with their respective advantages and limitations. Therefore, this paper proposes a convolutional autoencoder model for exploiting the benefits of autoencoder and CNN models for CBIR system. In this way both the data and the class distribution are considered while producing the binary code for CBIR system. The proposed model is evaluated on CIFAR-10 training dataset considering ten classes and performance is also compared with the other existing models. The convolutional autoencoder model has produced better bit codes with more than 95% accuracy as compared to an autoencoder model and has outperformed state of the state of art autoencoder models used for CBIR.

关键词： autoencoder Binary codes CBIR CNN Deep Learning Image Classification

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Fed-LSAE: Thwarting poisoning attacks against federated cyber threat detection system via autoencoder-based latent space inspection

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JOURNAL OF INFORMATION SECURITY AND APPLICATIONS 2024年 87卷

作者： Luong, Tran Duc Tien, Vuong Minh Quyen, Nguyen Huu Hien, Do Thi Thu Duy, Phan The Pham, Van-Hau Univ Informat Technol Informat Secur Lab Ho Chi Minh City Vietnam Vietnam Natl Univ Ho Chi Minh City Vietnam

The rise of security concerns in conventional centralized learning has driven the adoption of federated learning. However, the risks posed by poisoning attacks from internal adversaries against federated systems necessitate robust anti-poisoning frameworks. While previous defensive mechanisms relied on outlier detection, recent approaches focus on latent space representation. In this paper, we investigate a novel robust aggregation method for federated learning, namely Fed-LSAE, which leverages latent space representation via the penultimate layer and autoencoder to exclude malicious clients from the training process. Specifically, Fed-LSAE measures the similarity level of each local latent space vector to the global one using the Center Kernel Alignment algorithm in every training round. The results of this algorithm are categorized into benign and attack groups, in which only the benign cluster is sent to the central server for federated averaging aggregation. In other words, adversaries would be detected and eliminated from the federated training procedure. The experimental results on the CIC-ToN-IoT and N-BaIoT datasets confirm the feasibility of our defensive mechanism against cutting- edge poisoning attacks for developing a robust federated-based threat detector in the Internet of Things (IoT) context. The evaluation of the federated approach witnesses an upward trend of approximately 98% across all metrics when integrating with our Fed-LSAE defense.

关键词： Federated learning Poisoning attack Threat detection autoencoder Penultimate layer representation Latent space

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A lightweight unsupervised adversarial detector based on autoencoder and isolation forest

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PATTERN RECOGNITION 2024年 147卷

作者： Liu, Hui Zhao, Bo Guo, Jiabao Zhang, Kehuan Liu, Peng Cent China Normal Univ Sch Comp Sci Wuhan 430079 Peoples R China Cent China Normal Univ Hubei Prov Key Lab Artificial Intelligence & Smart Wuhan 430079 Peoples R China Wuhan Univ Sch Cyber Sci & Engn Wuhan 430072 Peoples R China Chinese Univ Hong Kong Coll Informat Engn Hong Kong 999077 Peoples R China Penn State Univ Coll Informat Sci & Technol State Coll PA 16801 USA

Although deep neural networks (DNNs) have performed well on many perceptual tasks, they are vulnerable to adversarial examples that are generated by adding slight but maliciously crafted perturbations to benign images. Adversarial detection is an important technique for identifying adversarial examples before they are entered into target DNNs. Previous studies that were performed to detect adversarial examples either targeted specific attacks or required expensive computation. Designing a lightweight unsupervised detector is still a challenging problem. In this paper, we propose an autoencoder-based Adversarial Examples (AEAE) detector that can guard DNN models by detecting adversarial examples with low computation in an unsupervised manner. The AEAE includes only a shallow autoencoder that performs two roles. First, a well-trained autoencoder has learned the manifold of benign examples. This autoencoder can produce a large reconstruction error for adversarial images with large perturbations, so we can detect significantly perturbed adversarial examples based on the reconstruction error. Second, the autoencoder can filter out small noises and change the DNN's prediction on adversarial examples with small perturbations. It helps to detect slightly perturbed adversarial examples based on the prediction distance. To cover these two cases, we utilize the reconstruction error and prediction distance from benign images to construct a two-tuple feature set and train an adversarial detector using the isolation forest algorithm. We show empirically that AEAE is an unsupervised and inexpensive detector against most state-of-the-art attacks. Through the detection in these two cases, there is nowhere to hide adversarial examples.

关键词： Deep neural networks Adversarial examples Adversarial detection autoencoder Isolation forest

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A novel autoencoder for structural anomalies detection in river tunnel operation

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 244卷

作者： Tan, Xu-Yan Palaiahnakote, Shivakumara Chen, Weizhong Cheng, Ke Du, Bowen Chinese Acad Sci Inst Rock & Soil Mech State Key Lab Geomech & Geotech Engn Wuhan 430071 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Univ Malaya Dept Comp Syst & Technol Kuala Lumpur Malaysia Beihang Univ SKLSDE Lab Beijing 100191 Peoples R China

Anomaly diagnosis is essential to prevent disasters and ensure long-term stable operation of tunnels. However, the diversity and scarcity of abnormal data make it difficult to identify outliers, especially to diagnose structural anomalies from poor-quality data. Therefore, an adaptive loss function improved autoencoder (AdaAE) model is proposed for anomaly detection, which is robust to poor-quality data and can accurately determine the anomaly source of river tunnel. To expand the abnormal dataset, numerical simulation of structure under extreme conditions and Gaussian noise are adopted to construct structural damage data and disturbance data respectively. The proposed model is then instantiated on the prepared dataset. Finally, the reliability and the advantage of the proposed model are verified by ablation study. The research results indicate that the detection ability of AdaAE model is greatly improved to that of the widely used methods. This model is suitable to poor quality dataset, and the accuracy to detect structural anomalies from pollution data sets is more than 90%. As a case study, the AdaAE model is applied to the Wuhan Yangtze River tunnel to detect anomalies of segment strain. This study would play a role in preventing structural diseases and promoting intelligent management during tunnel operation.

关键词： Tunnel Machine learning Anomaly autoencoder Extreme conditions Gaussian noise

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DSVD-autoencoder: A scalable distributed privacy-preserving method for one-class classification

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INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS 2021年第1期36卷 177-199页

作者： Fontenla-Romero, Oscar Perez-Sanchez, Beatriz Guijarro-Berdinas, Bertha Univ A Coruna CITIC Fac Informat Dept Comp Sci & Informat Technol Campus Elvina S-N La Coruna 15071 Spain

One-class classification has gained interest as a solution to certain kinds of problems typical in a wide variety of real environments like anomaly or novelty detection. autoencoder is the type of neural network that has been widely applied in these one-class problems. In the Big Data era, new challenges have arisen, mainly related with the data volume. Another main concern derives from Privacy issues when data is distributed and cannot be shared among locations. These two conditions make many of the classic and brilliant methods not applicable. In this paper, we present distributed singular value decomposition (DSVD-autoencoder), a method for autoencoders that allows learning in distributed scenarios without sharing raw data. Additionally, to guarantee privacy, it is noniterative and hyperparameter-free, two interesting characteristics when dealing with Big Data. In comparison with the state of the art, results demonstrate that DSVD-autoencoder provides a highly competitive solution to deal with very large data sets by reducing training from several hours to seconds while maintaining good accuracy.

关键词： autoencoder big data distributed learning neural network one-class classification privacy-preserving singular value decomposition

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Effectiveness of autoencoder for lake area extraction from high-resolution RGB imagery: an experimental study

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ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2021年第24期28卷 31084-31096页

作者： Tercan, Emre Atasever, Umit Haluk Gen Directorate Highways Dept Survey Project & Environm 13th Reg TR-07090 Antalya Turkey Erciyes Univ Fac Engn Dept Geomat Engn TR-38039 Kayseri Turkey

The surface areas of lakes alter constantly due to many factors such as climate change, land use policies, and human interventions, and their surface areas tend to decrease. It is necessary for obtain baseline datasets such as surface areas and boundaries of water bodies with high accuracy, effectively, economically, and practically by using satellite images in terms of management and planning of lakes. Extracting surface areas of water bodies using image classification algorithms and high-resolution RGB satellite images and evaluating the effectiveness of different image classification algorithms have become an important research domain. In this experimental study, eight different machine learning-based classification approaches, namely, k-nearest neighborhood (kNN), subspaced kNN, support vector machines (SVMs), random forest (RF), bagged tree (BT), Naive Bayes (NB), and linear discriminant (LD), have been utilized to extract the surface areas of lakes. Lastly, autoencoder (AE) classification algorithm was applied, and the effectiveness of all those algorithms was compared. Experimental studies were carried out on three different lakes (Hazar Lake, Salda Lake, Manyas Lake) using high-resolution Turkish RASAT RGB satellite images. The results indicated that AE algorithm obtained the highest accuracy values in both quantitative and qualitative analyses. Another important aspect of this study is that Structural Similarity Index (SSIM) and Universal Image Quality Index (UIQI) metrics that can evaluate close to human perception are used for comparison. With this application, it has been shown that overall accuracy calculated from test data may be inadequate in some cases by using SSIM, UIQI, mean squared error (MSE), peak signal to noise ratio (PSNR), and Cohen's KAPPA metrics. In the last application, the robustness of AE was examined with boxplots.

关键词： Machine learning autoencoder Remote sensing Image classification Water body extraction RASAT Environmental monitoring Lake management

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BLER performance evaluation of an enhanced channel autoencoder

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COMPUTER COMMUNICATIONS 2021年 176卷 173-181页

作者： Njoku, Judith Nkechinyere Morocho-Cayamcela, Manuel Eugenio Lim, Wansu Kumoh Natl Inst Technol Dept Aeronaut Mech & Elect Convergence Engn Gumi South Korea Yachay Tech Univ Sch Math & Computat Sci San Miguel De Urcuqui Ecuador

The concept of using autoencoders (AEs) to represent wireless communication systems as an end-to-end reconstruction task that optimizes the transmitter and receiver components simultaneously in a single process has attracted the attention of wireless practitioners worldwide. This is attributable to the flexibility, and convenience of representing complex channel models. However, owing to the characteristics of deep neural networks (DNNs), as the AE learns the representation of the channel, overfitting limits its performance. In this paper, we propose RegAE, a regularized DNN architecture that overcomes the overfitting limitation in AEs and reduces their training complexity, which are characteristics of models with higher dimensions. We demonstrate that RegAE improves the block error rate (BLER) as compared with equivalent models from the literature. Thereby, it achieves a performance (1) better than that of a 4/7 rate Hamming code with a 16 phase-shift keying (16PSK) modulation under an additive white Gaussian noise (AWGN) channel, (2) comparable to that of a 4/7 rate maximum likelihood decoding (MLD) with a E-b/N-0 range from 1 dB to 5 dB, and (3) equivalent to that of an uncoded binary phase-shift keying (BPSK) modulation over a E-b/N-0 range from 0 dB to 10 dB.

关键词： autoencoder Deep learning Neural network Wireless communications Modulation End-to-end learning

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Real-Time Outlier Detection Applied to a Doppler Velocity Log Sensor Based on Hybrid autoencoder and Recurrent Neural Network

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IEEE JOURNAL OF OCEANIC ENGINEERING 2021年第4期46卷 1288-1301页

作者： Davari, Narjes Aguiar, A. Pedro Univ Porto Fac Engn P-4200465 Porto Portugal

This article presents a real-time outlier detection deep-learning (OD-DL)-based method using a hybridized artificial neural network (ANN) approach. We propose an unsupervised ANN scheme that runs in parallel, a denoising autoencoder (DAE) and a recurrent neural network (RNN). The DAE aims to reconstruct relevant/normal input data, whereas it seeks to ignore outliers;the RNN, with a recursive structure, is used to predict time-series data. As measurements arrive, two tasks are performed: 1) the outlier decision, which is based on a reconstruction error and an energy score criteria from the output difference between the DAE and the RNN;and 2) the training procedure for both DAE and RNN. The proposed OD-DL scheme is specifically targeted to address the outlier problem of the data generated by a Doppler velocity log (DVL) sensor installed on board of an autonomous underwater vehicle (AUV) to enhance the AUV navigation system performance. In particular, the DVL data enter into the OD-DL scheme whose output is fed into an AUV navigation system that runs an error-state Kalman filter that integrates the corrected DVL data with the measurements of an inertial measurement unit and a depth meter. The experimental results show that the AUV navigation system with the OD-DL method outperforms in terms of a more accurate estimated position when compared with the case that there is no outlier detection and with the case of a navigation system using a conventional outlier detection method, or other simpler deep-learning methods.

关键词： Navigation Neurons Recurrent neural networks Kalman filters Training Real-time systems Anomaly detection autoencoder autonomous underwater vehicle (AUV) error-state Kalman filter (ESKF) integrated navigation system outlier detection (OD) recurrent neural network (RNN)

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