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Griffin: Real-Time Network Intrusion Detection System via Ensemble of autoencoder in SDN

IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT 2022年第3期19卷 2269-2281页

作者： Yang, Liyan Song, Yubo Gao, Shang Hu, Aiqun Xiao, Bin Southeast Univ Sch Cyber Sci & Engn Nanjing 210096 Peoples R China Southeast Univ Frontiers Sci Ctr Mobile Informat Commun & Secur Nanjing 210096 Peoples R China Purple Mt Labs Nanjing 211189 Peoples R China Hong Kong Polytech Univ Dept Comp Hong Kong Peoples R China

Many efforts have been devoted to the development of efficient Network Intrusion Detection System (NIDS) using machine learning approaches in Software-defined Network (SDN). Unfortunately, existing solutions failed to detect real-time and zero-day attacks due to their limited throughput and prior knowledge-based detection. To this end, we propose Griffin, a NIDS that uses unsupervised machine learning expertise to detect both known and zero-day intrusion attacks in real-time with high accuracy. Specifically, Griffin uses an efficient feature extraction framework to capture the sequential features of the traffic packets. Then, it utilizes cluster analysis to reduce the feature scale to achieve low throughput. Moreover, an ensemble autoencoder is built automatically to further extract features with low complexity and high precision to train the model. We evaluate the accuracy, robustness, and complexity of the system using open datasets. The result shows that Griffin's complexity is about 40% lower, and its accuracy is at most 19% higher than existing ***, even in the situation with evasion, the Griffin has at most 9% decrease of AUC, which is a good performance compared with other solutions. Furthermore, this paper also utilizes the differential privacy framework during training autoencoders to protect datasets' privacy which is inherent in machine learning approaches.

关键词： Feature extraction Real-time systems Privacy Differential privacy Training Network intrusion detection Complexity theory Software-defined network (SDN) network intrusion detection system (NIDS) autoencoder ensemble learning differential privacy

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Bi-level multi-objective evolution of a Multi-Layered Echo-State Network autoencoder for data representations

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NEUROCOMPUTING 2019年 341卷 195-211页

作者： Chouikhi, Naima Ammar, Boudour Hussain, Amir Alimi, Adel M. Univ Sfax Natl Engn Sch Sfax ENIS REs Grp Intelligent Machines REGIM Lab BP 1173 Sfax 3038 Tunisia Edinburgh Napier Univ Sch Comp Merchiston Campus Edinburgh EH10 5DT Midlothian Scotland

The Multi-Layered Echo-State Network (ML-ESN) is a recently developed, highly powerful type of recurrent neural network. It has succeeded in dealing with several non-linear benchmark problems. On account of its rich dynamics, ML-ESN is exploited in this paper, for the first time, as a recurrent autoencoder (ML-ESNAE) to extract new features from original data representations. Further, the challenging and crucial task of optimally determining the ML-ESNAE architecture and training parameters is addressed, in order to extract more efficient features from the data. Traditionally, in a ML-ESN, the number of parameters (hidden neurons, sparsity rates, weights) are randomly chosen and manually altered to achieve a minimum learning error. On one hand, this random setting may not guarantee best generalization results. On the other, it can increase the network's complexity. In this paper, a novel bi-level evolutionary optimization approach is thus proposed for the ML-ESNAE, to deal with these challenges. The first level offers Pareto multi-objective architecture optimization, providing maximum learning accuracy while maintaining a reduced complexity target. Next, every Pareto optimal solution obtained from the first level undergoes a mono-objective weights optimization at the second level. Particle Swarm Optimization (PSO) is used as an evolutionary tool for both levels 1 and 2. An empirical study shows that the evolved ML-ESNAE produces a noticeable improvement in extracting new, more expressive data features from original ones. A number of application case studies, using a range of benchmark datasets, show that the extracted features produce excellent results in terms of classification accuracy. The effectiveness of the evolved ML-ESNAE is demonstrated for both noisy and noise-free data. In conclusion, the evolutionary ML-ESNAE is proposed as a new benchmark for the evolutionary AI and machine learning research community. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Multi-Layered Echo State Network autoencoder Data representation PSO Multi-objective optimization Architecture optimization Weights optimization

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A new health indicator extracted by unsupervised learning using autoencoder in tandem with t-sne and multi-kernel CNN to enhance the early detection and classification of bearings multi-faults

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JOURNAL OF THE BRAZILIAN SOCIETY OF MECHANICAL SCIENCES AND ENGINEERING 2023年第7期45卷 1-14页

作者： Zair, Mohamed Rahmoune, Chemseddine Imane, Moussaoui Amine, Mahami Benazzouz, Djamel Univ Mhamed Bougara Boumerdes Solid Mech & Syst Lab LMSS Boumerdes 35000 Algeria

The conventional process diagnostic scheme comprises data acquisition, feature extraction, and fault classification. However, traditional feature extraction uses signal processing technologies that are deeply reliant on both subjectivity and foreknowledge. However, these techniques have shown some limitations that are affecting efficiency and effectiveness, especially for varying fault and noisy environments. To address these issues, this paper proposes a practical and effective unsupervised deep learning methodology based on autoencoder (AE) in tandem with t-distributed stochastic neighbor embedding (t-sne) and multi-kernel convolutional neural network. In this approach, the raw data are first extracted in the time domain, normalizing and segmenting the vibration signal into small portions by sliding window to keep more information data. Subsequently, during autoencoder (AE) training, the dropout smoothing and batch normalization are used to avoid overfitting and to extract the deep features of the retread form from the normalized data set in the time domain. Then, the nonlinear mapping obtained in the high-dimensional data is reduced using the t-sne algorithm by deleting redundant and insignificant parameters that can confuse the classification. Finally, the measurement with low-dimensional feature vectors is selected as inputs of the deep structure of multi-kernel CNN for automatic fault detection and classification. Comparative studies were implemented with several techniques including CNN, KNN and SVM to diagnose and differentiate different types of defects in bearings. The results showed that the proposed approach is effective for bearings in terms of predictive accuracy with higher accuracy 99.46% compared to conventional methods.

关键词： Rolling bearings autoencoder Visualization data Faults diagnosis t-Distributed stochastic neighbor embedding (t-sne) Fault classification Multi-kernel conventional neural network (CNN)

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LoRa DL: a deep learning model for enhancing the data transmission over LoRa using autoencoder

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JOURNAL OF SUPERCOMPUTING 2023年第15期79卷 17079-17097页

作者： Shilpa, B. Kumar, Puranam Revanth Jha, Rajesh Kumar ICFAI Fdn Higher Educ Fac Sci & Technol IcfaiTech Dept Elect & Commun Engn Hyderabad India

Low Power Wide Area Networks (LPWANs) have become a popular option for modern wireless communication technologies. Long Range (LoRa) protocol was designed for LPWAN, which offers long-distance communication, low-power consumption, and simultaneous transmissions. Long-distance communication necessitates an extremely low signal-to-noise ratio at the receiver. Additionally, low power consumption necessitates less signaling, which leads to the usage of simpler protocols like ALOHA and less coordinated communication. Therefore, as the number of devices equipped with this technology grows, its performance will degrade naturally as a result of scalability and interference difficulties. Deep learning holds great promise for resolving these problems through data-driven approaches and enhances the efficiency of LoRaWAN in usage of limited spectrum resources. In this work, we present the design of an end-to-end communication system as an autoencoder architecture utilizing deep learning. This adaptable architecture is able to efficiently capture channel impairments while simultaneously optimizing the operations of the transmitter and receiver together. The autoencoder is designed with multiple system model elements, including a sender net that simulates a LoRa transmitter and modulates data, a channel net that models channel impairments, and a receiver net that acts as a LoRa receiver to demodulate and retrieve the original data. The proposed autoencoder model is trained and evaluated with LoRa samples generated by simulation, and it is shown to be a high performer by comparing with the traditional LoRaWAN in terms of Bit Error Rate (BER) and Packet Success Rate (PSR) measurements.

关键词： autoencoder Deep learning Internet of Things LoRa Wireless Communication

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Underwater Wireless Optical Communication With One-Bit Quantization: A Hybrid autoencoder and Generative Adversarial Network Approach

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2023年第10期22卷 6432-6444页

作者： Zou, Cong Yang, Fang Song, Jian Han, Zhu Tsinghua Univ Beijing Natl Res Ctr Informat Sci & Technol Dept Elect Engn Beijing 100084 Peoples R China Tsinghua Univ Key Lab Digital TV Syst Guangdong Prov & Shenzhen Res Inst Shenzhen 518057 Peoples R China Tsinghua Univ Shenzhen Int Grad Sch Shenzhen 518055 Peoples R China Univ Houston Dept Elect & Comp Engn Houston TX 77004 USA Kyung Hee Univ Dept Comp Sci & Engn Seoul 446701 South Korea

Compared with underwater acoustic communication, underwater wireless optical communication (UWOC) has the advantages of wide communication bandwidth, high transmission speed and strong directional transmission, which make it have broad application prospects. However, the absorption and scattering effects in underwater environments cause a UWOC system to suffer from severe channel fading. To reduce the system complexity and power loss, the on- -off-keying (OOK) modulation scheme and one-bit analog-to-digital converter are considered in this work. Besides, to deal with the complex underwater optical channels and the nonlinearity of the one-bit quantization, a novel deep learning based architecture integrating the autoencoder (AE) and generative adversarial network (GAN), named hybrid AE-GAN, is developed. In the proposed hybrid AE-GAN, the generator in GAN is responsible for a generalized channel equalization, which aims to equalize both the impairments from underwater optical channels and the distortion from one-bit quantization. While the decoder in AE and the discriminator in GAN share the same network to distinguish true and fake as well as reconstruct the sent messages simultaneously. Meanwhile, the one-bit quantized signal is regarded as conditional information for GAN to assist in targeted channel equalization for various signals. Moreover, a specialized loss function and training strategy for the hybrid AE-GAN are also presented, which can optimize AE and GAN at the same time, and deal with the problem that the binarization of OOK stymies the gradient backpropagation. The simulation results indicate that the proposed hybrid AE-GAN can achieve superior performance.

关键词： Underwater wireless optical communication autoencoder generative adversarial network channel equalization one-bit quantization

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Multiple Fault Diagnosis in a Wind Turbine Gearbox with autoencoder Data Augmentation and KPCA Dimension Reduction

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JOURNAL OF NONDESTRUCTIVE EVALUATION 2024年第4期43卷 1-16页

作者： Felix, Leonardo Oldani Martins, Dionisio Henrique Carvalho de Sa So Monteiro, Ulisses Admar Barbosa Vicente Pinto, Luiz Antonio Vaz Tarrataca, Luis Martins, Carlos Alfredo Orfao Fed Univ Rio de Janeiro UFRJ Ocean Engn Program PENO Ctr Tecnol Bloco 1-108Cidade Univ BR-20945970 Rio De Janeiro RJ Brazil Fed Ctr Technol Educ Rio de Janeiro BR-25620003 Rio De Janeiro RJ Brazil

Gearboxes, as critical components, often operate in demanding conditions, enduring constant exposure to variable loads and speeds. In the realm of condition monitoring, the dataset primarily comprises data from normal operating conditions, with significantly fewer instances of faulty conditions, resulting in imbalanced datasets. To address the challenges posed by this data disparity, researchers have proposed various solutions aimed at enhancing the performance of classification models. One such solution involves balancing the dataset before the training phase through oversampling techniques. In this study, we utilized the Sparse autoencoder technique for data augmentation and employed Support Vector Machine (SVM) and Random Forest (RF) for classification. We conducted four experiments to evaluate the impact of data imbalance on classifier performance: (1) using the original dataset without data augmentation, (2) employing partial data augmentation, (3) applying full data augmentation, and (4) balancing the dataset while using Kernel Principal Component Analysis (KPCA) for dimensionality reduction. Our findings revealed that both algorithms achieved accuracies exceeding 90%, even when employing the original non-augmented data. When partial data augmentation was employed both algorithms were able to achieve accuracies beyond 98%. Full data augmentation yielded slightly better results compared to partial augmentation. After reducing dimensions from 18 to 11 using KPCA, both classifiers maintained robust performance. SVM achieved an overall accuracy of 98.72%, while RF achieved 96.06% accuracy.

关键词： Gearbox Fault diagnosis autoencoder KPCA SVM Random Forest

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Role Discovery-Guided Network Embedding Based on autoencoder and Attention Mechanism

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IEEE TRANSACTIONS ON CYBERNETICS 2023年第1期53卷 365-378页

作者： Jiao, Pengfei Tian, Qiang Zhang, Wang Guo, Xuan Jin, Di Wu, Huaming Tianjin Univ Law Sch Coll Intelligence & Comp Tianjin 300350 Peoples R China Tianjin Univ Law Sch Ctr Biosafety Res & Strategy Tianjin 300350 Peoples R China Tianjin Univ Coll Intelligence & Comp Tianjin 300350 Peoples R China Tianjin Univ Ctr Appl Math Tianjin 300072 Peoples R China

Recently, network embedding (NE) is an amazing research point in complex networks and devoted to a variety of tasks. Nearly, all the methods and models of NE are based on the local, high-order, or global similarity of the networks, and few studies have focused on the role discovery or structural similarity, which is of great significance in spreading dynamics and network theory. Meanwhile, existing NE models for role discovery suffer from two limitations, that is: 1) they fail to model the varying dependencies between each node and its neighbor nodes and 2) they cannot capture the effective node features which are helpful to role discovery, which makes these methods ineffective when applied to the role discovery task. To solve the above problems of NE for role discovery or structural similarity, we propose a unified deep learning framework, called RDAA, which can effectively represent features of nodes and benefit the Role Discovery-guided NE with a deep autoencoder, while modeling the local links with an Attention mechanism. In addition, we design an elaborately binding technique to combine both parts and optimize the framework in a unified way. We conduct different experiments, including visualization, role classification, role discovery, and running time compared to popular NE methods for both proximity and structural similarity. The RDAA has better performance on all the datasets and achieves good tradeoffs.

关键词： Feature extraction Task analysis Social networking (online) Deep learning Matrix decomposition Complex networks Neural networks Attention mechanism autoencoder network representation role discovery structural similarity

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Multi-Modal Stacked Denoising autoencoder for Handling Missing Data in Healthcare Big Data

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IEEE ACCESS 2020年 8卷 104933-104943页

作者： Kim, Joo-Chang Chung, Kyungyong Kyonggi Univ Dept Comp Sci Suwon 16227 South Korea Kyonggi Univ Div Comp Sci & Engn Suwon 16227 South Korea

Supply and demand increase in response to healthcare trends. Moreover, personal health records (PHRs) are being managed by individuals. Such records are collected using different avenues and vary considerably in terms of their type and scope depending on the particular circumstances. As a result, some data may be missing, which has a negative effect on the data analysis, and such data should, therefore, be replaced with appropriate values. In this study, a method for estimating missing data using a multi-modal autoencoder applied to the field of healthcare big data is proposed. The proposed method uses a stacked denoising autoencoder to estimate the missing data that occur during the data collection and processing stages. autoencoders are neural networks that output value of x(boolean AND) similar to an input value of x. In the present study, data from the Korean National Health Nutrition Examination Survey (KNHNES), conducted by the Korea Centers for Disease Control and Prevention (KCDC), are used. As representative healthcare data from South Korea, they contain a large number of parameters identical to those used in the PHRs. Based on this, models can be generated to estimate missing data occurring in PHRs. Furthermore, PHRs involve a multi-modality that allows the data to be collected from multiple sources for a single object. Therefore, the stacked denoising autoencoder applied is configured under a multi-modal setting. Through pre-processing, a set of data without missing value in KNHNES is designed. In the data set based learning, a label is set as original data, and an autoencoder input is set as noised input that additionally has as many random zero numbers as noise factor. In this way, the autoencoder learns in the way of making the zero-based noise value similar to the original label value. When the amount of missing data in a dataset reaches approximately 25%, the accuracy of the proposed method using a multi-modal stacked denoising autoencoder is 0.9217,

关键词： Medical services Big Data Noise reduction Machine learning Data models Computational modeling Estimation autoencoder data pre-processing data estimation data imputation health big data multi-modal missing data machine learning

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autoencoder-based CSI Feedback with Adjustable Feedback Payload Size 59

Autoencoder-based CSI Feedback with Adjustable Feedback Payl...

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59th Annual IEEE International Conference on Communications (IEEE ICC)

作者： Yao, Shuang Ying, Dawei Li, Clara Intel Corp Santa Clara CA 95051 USA

ISBN: (纸本)9781728190549

Channel state information (CSI) feedback enhancement based on artificial intelligence (AI)/machine learning (ML) has been widely studied in the recent years, and has been selected as one of three physical layer AI/ML use cases in 3GPP Release 18 [1]. Despite their success of achieving a better trade-off between feedback accuracy and feedback payload size, most of the current solutions lack the flexibility to adjust to feedback payload size. For different feedback payload sizes, separate ML models are trained and utilized, resulting in a linear increase of the number of ML models with respect to the number of feedback payload sizes. It can be problematic for real-world deployment, since one ML model usually has hundreds of thousands or even millions of parameters. In this paper, we build an ML model where the majority of model parameters are shared, while the normalization layer is switched according to feedback payload size. The feasibility and effectiveness of switchable normalization layer is verified through numerical simulations, where an autoencoder (AE) is leveraged for CSI feedback and both convolutional neural network (CNN) and transformer based AE are considered. Results show that switchable normalization layer can adapt to various quantization bit-widths as well as various encoder output widths, achieving similar or sometimes even better performance than having separate AEs. Furthermore, by re-using most of the model parameters, the increase in model size is negligible.

关键词： MIMO CSI feedback AI/ML autoencoder

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autoencoder Optimization for Anomaly Detection: A Comparative Study with Shallow Algorithms

Autoencoder Optimization for Anomaly Detection: A Comparativ...

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International Joint Conference on Neural Networks (IJCNN)

作者： Kumar, Vikas Srivastava, Vishesh Mahjabin, Sadia Pal, Arindam Kluettermann, Simon Mueller, Emmanuel TU Dortmund Univ Dortmund Germany

ISBN: (纸本)9798350359329;9798350359312

This paper presents an innovative guide for optimizing autoencoder performance, specifically targeting anomaly detection tasks. In addressing prevalent issues in deep learning algorithms, our primary focus lies in effectively selecting and controlling the latent space in autoencoders. We comprehensively explore methodologies for determining the optimal latent size, a critical and often overlooked aspect in autoencoder architectures. This endeavor forms part of a broader initiative to enhance autoencoder efficacy, ensuring their performance is on par with or superior to many shallow learning algorithms, a challenge highlighted in studies like ADBENCH. Our approach encompasses a detailed examination and experimentation with various parameters, architectures, and loss functions, all aimed at refining the efficiency and accuracy of autoencoders in anomaly detection for image and tabular data. This research stands out for its dual focus on image and tabular datasets. We thoroughly examine the performance of autoencoders in detecting anomalies, utilizing a variety of autoencoder architectures and diverse hyperparameters.

关键词： autoencoder Anomaly Detection Hyperparameter

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