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Towards an Effective Intrusion Detection Model Using Focal Loss variational autoencoder for Internet of Things (IoT)

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SENSORS 2022年第15期22卷 5822页

作者： Khanam, Shapla Ahmedy, Ismail Idris, Mohd Yamani Idna Jaward, Mohamed Hisham Univ Malaya Fac Comp Sci & Informat Technol Dept Comp Syst & Technol Kuala Lumpur 50603 Malaysia Univ Malaya Fac Comp Sci & Informat Technol Ctr Mobile Cloud Comp Kuala Lumpur 50603 Malaysia Monash Univ Malaysia Sch Engn Bandar Sunway 47500 Malaysia

As the range of security attacks increases across diverse network applications, intrusion detection systems are of central interest. Such detection systems are more crucial for the Internet of Things (IoT) due to the voluminous and sensitive data it produces. However, the real-world network produces imbalanced traffic including different and unknown attack types. Due to this imbalanced nature of network traffic, the traditional learning-based detection techniques suffer from lower overall detection performance, higher false-positive rate, and lower minority-class attack detection rates. To address the issue, we propose a novel deep generative-based model called Class-wise Focal Loss variational autoencoder (CFLVAE) which overcomes the data imbalance problem by generating new samples for minority attack classes. Furthermore, we design an effective and cost-sensitive objective function called Class-wise Focal Loss (CFL) to train the traditional variational autoencoder (VAE). The CFL objective function focuses on different minority class samples and scrutinizes high-level feature representation of observed data. This leads the VAE to generate more realistic, diverse, and quality intrusion data to create a well-balanced intrusion dataset. The balanced dataset results in improving the intrusion detection accuracy of learning-based classifiers. Therefore, a Deep Neural Network (DNN) classifier with a unique architecture is then trained using the balanced intrusion dataset to enhance the detection performance. Moreover, we utilize a challenging and highly imbalanced intrusion dataset called NSL-KDD to conduct an extensive experiment with the proposed model. The results demonstrate that the proposed CFLVAE with DNN (CFLVAE-DNN) model obtains promising performance in generating realistic new intrusion data samples and achieves superior intrusion detection performance. Additionally, the proposed CFLVAE-DNN model outperforms several state-of-the-art data generation and traditi

关键词： Internet of Things variational autoencoder Class-wise Focal Loss data imbalance intrusion detection Deep Neural Network

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Image Super-Resolution Based on variational autoencoder and Channel Attention 23

Image Super-Resolution Based on Variational Autoencoder and ...

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Proceedings of the 2023 6th International Conference on Artificial Intelligence and Pattern Recognition

作者： Jian Xu Yurong Zhao Xi'an University of Posts and Telecommunications China

ISBN: (纸本)9798400707674

Super-resolution (SR) method based on generative adversarial networks (GANs) has achieved excellent performance in both visual perception and image quality. However, there is still room for improvement. Therefore, we propose a variational autoencoder (VAE) network architecture. The VAE encoder can learn the probability distribution of the low-resolution (LR) image and reflect the probability with a latent variable, and the decoder restores the original image through latent variables. The VAE and discriminator work together to effectively distinguish between generated images and real high-resolution (HR) images. In addition, we introduce a channel attention (CA) mechanism into the discriminator to improve the cohesion between channels and extract useful features more effectively. With the help of VAE and CA, the proposed method achieves not only higher peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) values, but also more realistic visual quality. The experimental results verify the feasibility of the proposed method.

关键词： Channel attention Discriminator Image super-resolution variational autoencoder

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Dimensionality reduction and visualization of single-cell RNA-seq data with an improved deep variational autoencoder

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BRIEFINGS IN BIOINFORMATICS 2023年第3期24卷 bbad152-bbad152页

作者： Jiang, Jing Xu, Junlin Liu, Yuansheng Song, Bosheng Guo, Xiulan Zeng, Xiangxiang Zou, Quan Hunan Univ Changsha Peoples R China Zhangjiajie Peoples Hosp Zhangjiajie Peoples R China Hunan Univ Coll Comp Sci & Elect Engn Changsha Peoples R China Univ Elect Sci & Technol China Inst Fundamental & Frontier Sci Chengdu Peoples R China

Single-cell RNA sequencing (scRNA-seq) is a revolutionary breakthrough that determines the precise gene expressions on individual cells and deciphers cell heterogeneity and subpopulations. However, scRNA-seq data are much noisier than traditional high-throughput RNA-seq data because of technical limitations, leading to many scRNA-seq data studies about dimensionality reduction and visualization remaining at the basic data-stacking stage. In this study, we propose an improved variational autoencoder model (termed DREAM) for dimensionality reduction and a visual analysis of scRNA-seq data. Here, DREAM combines the variational autoencoder and Gaussian mixture model for cell type identification, meanwhile explicitly solving 'dropout' events by introducing the zero-inflated layer to obtain the low-dimensional representation that describes the changes in the original scRNA-seq dataset. Benchmarking comparisons across nine scRNA-seq datasets show that DREAM outperforms four state-of-the-art methods on average. Moreover, we prove that DREAM can accurately capture the expression dynamics of human preimplantation embryonic development.

关键词： dropout variational autoencoder visualization dimensionality reduction single-cell RNA-seq

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Nearest Neighbours Graph variational autoencoder

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ALGORITHMS 2023年第3期16卷 143-143页

作者： Arsini, Lorenzo Caccia, Barbara Ciardiello, Andrea Giagu, Stefano Mancini Terracciano, Carlo Sapienza Univ Rome Dept Phys I-00185 Rome Italy INFN Sect Rome I-00185 Rome Italy Ist Super Sanita I-00161 Rome Italy

Graphs are versatile structures for the representation of many real-world data. Deep Learning on graphs is currently able to solve a wide range of problems with excellent results. However, both the generation of graphs and the handling of large graphs still remain open challenges. This work aims to introduce techniques for generating large graphs and test the approach on a complex problem such as the calculation of dose distribution in oncological radiotherapy applications. To this end, we introduced a pooling technique (ReNN-Pool) capable of sampling nodes that are spatially uniform without computational requirements in both model training and inference. By construction, the ReNN-Pool also allows the definition of a symmetric un-pooling operation to recover the original dimensionality of the graphs. We also present a variational autoencoder (VAE) for generating graphs, based on the defined pooling and un-pooling operations, which employs convolutional graph layers in both encoding and decoding phases. The performance of the model was tested on both the realistic use case of a cylindrical graph dataset for a radiotherapy application and the standard benchmark dataset sprite. Compared to other graph pooling techniques, ReNN-Pool proved to improve both performance and computational requirements.

关键词： graph neural network variational autoencoder pooling nearest neighbours

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Detecting Web Attacks From HTTP Weblogs Using variational LSTM autoencoder Deviation Network

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IEEE TRANSACTIONS ON SERVICES COMPUTING 2024年第5期17卷 2210-2222页

作者： Jagat, Rikhi Ram Sisodia, Dilip Singh Singh, Pradeep Natl Inst Technol Raipur Dept Comp Sci & Engn Raipur 492010 India

Web attacks penetrate the web applications' security through unauthorized access to sensitive information, disrupting services, and stealing data. Conventionally, rule-based statistical methods distinguish attackers from legitimate users. However, the training through manually extracted weblog features is time-consuming and requires subject expertise. Additionally, the supervised attack classification method needs massive, labeled weblog data, which is expensive and unfeasible. Also, the unsupervised classification techniques have resolved the labeled data insufficiency problem, but their detection performance is unreliable. Recent studies focus on recognizing web attacks through deep neural network-based anomaly detection. Hence, this study proposes an anomaly detection-based variational LSTM autoencoder Deviation Network (VLADEN) for recognizing web attacks from weblogs. This work resolves the aforementioned issues by extracting the aberrant information encoded in weblog request data to detect web attacks. VLADEN works in three stages: data preprocessing, anomaly and reference score generation, and classification. The variational LSTM self-encoding-based reference score generation ensures that the anomaly score deviates from the normal data. The proposed model is experimentally validated on three publicly available datasets (CSIS2010, FWAF, and HTTPParams) and evaluated using AUC-ROC and AUC-PR-based evaluation metrics. The results demonstrate the models' superior performance in detecting attack requests with minimum domain knowledge and labeled data.

关键词： Feature extraction Anomaly detection Long short term memory Security SQL injection HTTP Deep learning deep learning deviation network long short-term memory variational autoencoder web attack weblog

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Adversarial and Contrastive variational autoencoder for Sequential Recommendation 21

Adversarial and Contrastive Variational Autoencoder for Sequ...

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30th World Wide Web Conference (WWW)

作者： Xie, Zhe Liu, Chengxuan Zhang, Yichi Lu, Hongtao Wang, Dong Ding, Yue Shanghai Jiao Tong Univ Dept Comp Sci & Engn Shanghai Peoples R China Shanghai Jiao Tong Univ Sch Cyber Sci & Engn Shanghai Peoples R China Shanghai Jiao Tong Univ Univ Michigan Shanghai Jiao Tong Univ Joint Inst Shanghai Peoples R China Shanghai Jiao Tong Univ Sch Software Shanghai Peoples R China

ISBN: (纸本)9781450383127

Sequential recommendation as an emerging topic has attracted increasing attention due to its important practical significance. Models based on deep learning and attention mechanism have achieved good performance in sequential recommendation. Recently, the generative models based on variational autoencoder (VAE) have shown the unique advantage in collaborative filtering. In particular, the sequential VAE model as a recurrent version of VAE can effectively capture temporal dependencies among items in user sequence and perform sequential recommendation. However, VAE-based models suffer from a common limitation that the representational ability of the obtained approximate posterior distribution is limited, resulting in lower quality of generated samples. This is especially true for generating sequences. To solve the above problem, in this work, we propose a novel method called Adversarial and Contrastive variational autoencoder (ACVAE) for sequential recommendation. Specifically, we first introduce the adversarial training for sequence generation under the Adversarial variational Bayes (AVB) framework, which enables our model to generate high-quality latent variables. Then, we employ the contrastive loss. The latent variables will be able to learn more personalized and salient characteristics by minimizing the contrastive loss. Besides, when encoding the sequence, we apply a recurrent and convolutional structure to capture global and local relationships in the sequence. Finally, we conduct extensive experiments on four real-world datasets. The experimental results show that our proposed ACVAE model outperforms other state-of-the-art methods.

关键词： Sequential Recommendation variational autoencoder Adversarial Learning Contrastive Learning

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Bilateral variational autoencoder for Collaborative Filtering 21

Bilateral Variational Autoencoder for Collaborative Filterin...

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14th ACM International Conference on Web Search and Data Mining (WSDM)

作者： Quoc-Tuan Truong Salah, Aghiles Lauw, Hady W. Singapore Management Univ Sch Informat Syst Singapore Singapore

ISBN: (纸本)9781450382977

Preference data is a form of dyadic data, with measurements associated with pairs of elements arising from two discrete sets of objects. These are users and items, as well as their interactions, e.g., ratings. We are interested in learning representations for both sets of objects, i.e., users and items, to predict unknown pairwise interactions. Motivated by the recent successes of deep latent variable models, we propose Bilateral variational autoencoder (BiVAE), which arises from a combination of a generative model of dyadic data with two inference models, user- and item-based, parameterized by neural networks. Interestingly, our model can take the form of a Bayesian variational autoencoder either on the user or item side. As opposed to the vanilla VAE model, BiVAE is "bilateral", in that users and items are treated similarly, making it more apt for two-way or dyadic data. While theoretically sound, we formally show that, similarly to VAE, our model might suffer from an over-regularized latent space. This issue, known as posterior collapse in the VAE literature, may appear due to assuming an over-simplified prior (isotropic Gaussian) over the latent space. Hence, we further propose a mitigation of this issue by introducing constrained adaptive prior (CAP) for learning user-and item-dependent prior distributions. Empirical results on several real-world datasets show that the proposed model outperforms conventional VAE and other comparative collaborative filtering models in terms of item recommendation. Moreover, the proposed CAP further boosts the performance of BiVAE. An implementation of BiVAE is available on Cornac recommender library.

关键词： Collaborative Filtering variational autoencoder Dyadic Data

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Convolutional One-Dimensional variational autoencoder Based Intrusion detection 19

Convolutional One-Dimensional Variational Autoencoder Based ...

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6th IEEE Cyber Science and Technology Congress (CyberSciTech)

作者： Wu, Kailin Cao, MengTing Wang, Pan Wang, ZiXuan Nanjing Univ Posts & Telecommun Sch Modern Posts Nanjing Peoples R China

ISBN: (纸本)9781665421744

Intrusion detection technology is an important part of network security. It detects various intrusions by collecting and analyzing various information on the network, which is the focus of maintaining network *** the popularization of the network and the increase of the network rate, the network attack behavior is increasing and the attack methods are constantly updated. The traditional detection technology can not meet the demand of the new network environment for network *** view of the defects of the existing intrusion detection algorithm model based on deep learning, such as long training time, more hyperparameters and high data demand, it is feasible to detect malicious traffic by learning the characteristics of normal traffic. In this paper, a variational self-encoder (COD-VAE) intrusion detection model based on one-dimensional convolutional neural network is proposed, which can identify malicious traffic more *** intrusion detection algorithm is used to detect the intrusion of the intrusion detection *** results show that all the indexes of the algorithm are higher than the comparison algorithm, which verifies the effectiveness of the algorithm.

关键词： Intrusion detection Unsupervised Machine learning Deep learning variational autoencoder

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DISENTANGLED SPEECH REPRESENTATION LEARNING BASED ON FACTORIZED HIERARCHICAL variational autoencoder WITH SELF-SUPERVISED OBJECTIVE 31

DISENTANGLED SPEECH REPRESENTATION LEARNING BASED ON FACTORI...

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IEEE 31st International Workshop on Machine Learning for Signal Processing (MLSP)

作者： Xie, Yuying Arildsen, Thomas Tan, Zheng-Hua Aalborg Univ Dept Elect Syst Aalborg Denmark

ISBN: (纸本)9781728163383

Disentangled representation learning aims to extract explanatory features or factors and retain salient information. Factorized hierarchical variational autoencoder (FHVAE) presents a way to disentangle a speech signal into sequential-level and segmental-level features, which represent speaker identity and speech content information, respectively. As a self-supervised objective, autoregressive predictive coding (APC), on the other hand, has been used in extracting meaningful and transferable speech features for multiple downstream tasks. Inspired by the success of these two representation learning methods, this paper proposes to integrate the APC objective into the FHVAE framework aiming at benefiting from the additional self-supervision target. The main proposed method requires neither more training data nor more computational cost at test time, but obtains improved meaningful representations while maintaining disentanglement. The experiments were conducted on the TIMIT dataset. Results demonstrate that FHVAE equipped with the additional self-supervised objective is able to learn features providing superior performance for tasks including speech recognition and speaker recognition. Furthermore, voice conversion, as one application of disentangled representation learning, has been applied and evaluated. The results show performance similar to baseline of the new framework on voice conversion.

关键词： Disentangled representation learning variational autoencoder autoregressive predictive coding

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Generating and Blending Game Levels via Quality-Diversity in the Latent Space of a variational autoencoder 21

Generating and Blending Game Levels via Quality-Diversity in...

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16th International Conference on the Foundations of Digital Games (FDG)

作者： Sarkar, Anurag Cooper, Seth Northeastern Univ Boston MA 02115 USA

ISBN: (纸本)9781450384223

Several works have demonstrated the use of variational autoencoders (VAEs) for generating levels in the style of existing games and blending levels across different games. Further, quality-diversity (QD) algorithms have also become popular for generating varied game content by using evolution to explore a search space while focusing on both variety and quality. To reap the benefits of both these approaches, we present a level generation and game blending approach that combines the use of VAEs and QD algorithms. Specifically, we train VAEs on game levels and run the MAP-Elites QD algorithm using the learned latent space of the VAE as the search space. The latent space captures the properties of the games whose levels we want to generate and blend, while MAP-Elites searches this latent space to find a diverse set of levels optimizing a given objective such as playability. We test our method using models for 5 different platformer games as well as a blended domain spanning 3 of these games. We refer to using MAP-Elites for blending as Blend-Elites. Our results show that MAP-Elites in conjunction with VAEs enables the generation of a diverse set of playable levels not just for each individual game but also for the blended domain while illuminating game-specific regions of the blended latent space.

关键词： PCGML procedural content generation level generation quality diversity MAP-Elites variational autoencoder game blending

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