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Enhancing IoT Security: Federated Learning with GANs for Effective Attack Detection 20

Enhancing IoT Security: Federated Learning with GANs for Eff...

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20th International Bhurban Conference on Applied Sciences and Technology (IBCAST)

作者： Saeed, Bakhtawar Arshad, Sobia Saeed, Sanay Muhammad Umar Azam, Muhammad Awais UET Taxila Comp Engn Dept Taxila Pakistan UET Taxila Natl Ctr Cyber Secur NCCS Deep Packet Inspect Lab DPI Taxila Pakistan Whitecliffe Sch Informat Technol Technol Innovat Res Grp Auckland New Zealand

ISBN: (纸本)9798350308266;9798350308259

The rapid proliferation of the Internet of Things (IoT) has given rise to security challenges, necessitating the real-time detection and mitigation of cyberattacks. Federated Learning (FL) is promising because it enables collaborative global model training in IoT for attack detection while maintaining data privacy. However, FL-enabled systems for IoT face challenges related to class imbalance, inconsistent data distributions, and increased training time. This paper presents a novel FL-enabled approach for IoT attack detection that effectively resolves class imbalance by utilizing Generative Adversarial Networks (GANs). The proposed approach also handles non-iid data distributions through the implementation of Vertical FL. Additionally, ordinal encoding techniques are employed to enhance detection accuracy. The effectiveness of the proposed approach is extensively evaluated on the ToN-IoT and N-BaIoT datasets, the DAEs and MLPs both deep learning (DL) algorithms consistently models achieve high precision, recall, and F1 score on ToN-IoT (MLPs: 99.39%, DAEs: 99.62%) and N-BaIoT datasets (DAEs: 93.66%, MLPs: 94.63%), demonstrating its capability in improving attack detection accuracy and mitigating the limitations of FL-enabled systems in IoT scenarios.

关键词： Federated learning IoT deep autoencoder Anomaly detection Multilayer perceptron

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Fast Data Driven Estimation of Cluster Number in Multiplex Images using Embedded Density Outliers

Fast Data Driven Estimation of Cluster Number in Multiplex I...

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IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (IEEE CIBCB)

作者： Thomas, Spencer Angus Univ Surrey Dept Comp Sci Guildford Surrey England Natl Phys Lab NPL Data Sci Teddington Middx England

ISBN: (数字)9781665484626

ISBN: (纸本)9781665484626

The usage of chemical imaging technologies is becoming a routine accompaniment to traditional methods in pathology. Significant technological advances have developed these next generation techniques to provide rich, spatially resolved, multidimensional chemical images. The rise of digital pathology has significantly enhanced the synergy of these imaging modalities with optical microscopy and immunohistochemistry, enhancing our understanding of the biological mechanisms and progression of diseases. Techniques such as imaging mass cytometry provide labelled multidimensional (multiplex) images of specific components used in conjunction with digital pathology techniques. These powerful techniques generate a wealth of high dimensional data that create significant challenges in data analysis. Unsupervised methods such as clustering are an attractive way to analyse these data, however, they require the selection of parameters such as the number of clusters. Here we propose a methodology to estimate the number of clusters in an automatic data-driven manner using a deep sparse autoencoder to embed the data into a lower dimensional space. We compute the density of regions in the embedded space, the majority of which are empty, enabling the high density regions (i.e. clusters) to be detected as outliers and provide an estimate for the number of clusters. This framework provides a fully unsupervised and data-driven method to analyse multidimensional data. In this work we demonstrate our method using 45 multiplex imaging mass cytometry datasets. Moreover, our model is trained using only one of the datasets and the learned embedding is applied to the remaining 44 images providing an efficient process for data analysis. Finally, we demonstrate the high computational efficiency of our method which is two orders of magnitude faster than estimating via computing the sum squared distances as a function of cluster number.

关键词： Clustering Data driven analysis multiplex imaging deep autoencoder transfer learning hyperspectral data

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REPRESENTATIVE FUNCTIONAL CONNECTIVITY LEARNING FOR MULTIPLE CLINICAL GROUPS IN ALZHEIMER'S DISEASE 20

REPRESENTATIVE FUNCTIONAL CONNECTIVITY LEARNING FOR MULTIPLE...

引用

20th IEEE International Symposium on Biomedical Imaging (ISBI)

作者： Zhang, Lu Yu, Xiaowei Lyu, Yanjun Liu, Tianming Zhu, Dajiang Univ Texas Arlington Comp Sci & Engn Arlington TX 76019 USA Univ Georgia Comp Sci Athens GA USA

ISBN: (纸本)9781665473583

Mild cognitive impairment (MCI) is a high-risk dementia condition which progresses to probable Alzheimer's disease (AD) at approximately 10% to 15% per year. Characterization of group-level differences between two sub-types of MCI - stable MCI (sMCI) and progressive MCI (pMCI) is the key step to understand the mechanisms of MCI progression and enable possible delay of transition from MCI to AD. Functional connectivity (FC) is considered as a promising way to study MCI progression since which may show alterations even in preclinical stages and provide substrates for AD progression. However, the representative FC patterns during AD development for different clinical groups, especially for sMCI and pMCI, have been understudied. In this work, we integrated autoencoder and multi-class classification into a single deep model and successfully learned a set of clinical group related feature vectors. Specifically, we trained two non-linear mappings which realized the mutual transformations between the original FC space and the feature space. By mapping the learned clinical group related feature vectors to the original FC space, representative FCs were constructed for each group. Moreover, based on these feature vectors, our model achieves a high classification accuracy - 68% for multi-class classification (NC vs SMC vs sMCI vs pMCI vs AD). Code has been released(1).

关键词： MCI progression Representative group related FC deep autoencoder

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deep Anomaly Detection on Attributed Networks 19

Deep Anomaly Detection on Attributed Networks

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SIAM International Conference on Data Mining (SDM)

作者： Ding, Kaize Li, Jundong Bhanushali, Rohit Liu, Huan Arizona State Univ Comp Sci & Engn Tempe AZ 85281 USA

ISBN: (纸本)9781611975673

Attributed networks are ubiquitous and form a critical component of modern information infrastructure, where additional node attributes complement the raw network structure in knowledge discovery. Recently, detecting anomalous nodes on attributed networks has attracted an increasing amount of research attention, with broad applications in various high-impact domains, such as cybersecurity, finance, and healthcare. Most of the existing attempts, however, tackle the problem with shallow learning mechanisms by ego-network or community analysis, or through subspace selection. Undoubtedly, these models cannot fully address the computational challenges on attributed networks. For example, they often suffer from the network sparsity and data nonlinearity issues, and fail to capture the complex interactions between different information modalities, thus negatively impact the performance of anomaly detection. To tackle the aforementioned problems, in this paper, we study the anomaly detection problem on attributed networks by developing a novel deep model. In particular, our proposed deep model: (1) explicitly models the topological structure and nodal attributes seamlessly for node embedding learning with the prevalent graph convolutional network (GCN);and (2) is customized to address the anomaly detection problem by virtue of deep autoencoder that leverages the learned embeddings to reconstruct the original data. The synergy between GCN and autoencoder enables us to spot anomalies by measuring the reconstruction errors of nodes from both the structure and the attribute perspectives. Extensive experiments on real-world attributed network datasets demonstrate the efficacy of our proposed algorithm.

关键词： Anomaly Detection Attributed Networks Graph Convolutional Network deep autoencoder

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deep Streaming Graph Representations 18th

Deep Streaming Graph Representations

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18th International Conference on Computational Science (ICCS)

作者： Lei, Minglong Shi, Yong Li, Peijia Niu, Lingfeng Univ Chinese Acad Sci Sch Comp & Control Engn Beijing 100049 Peoples R China Univ Chinese Acad Sci Sch Econ & Management Beijing 100190 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China Chinese Acad Sci Res Ctr Fictitious Econ & Data Sci Beijing 100190 Peoples R China Univ Nebraska Coll Informat Sci & Technol Omaha NE 68182 USA

ISBN: (纸本)9783319937137;9783319937120

Learning graph representations generally indicate mapping the vertices of a graph into a low-dimension space, in which the proximity of the original data can be preserved in the latent space. However, traditional methods that based on adjacent matrix suffered from high computational cost when encountering large graphs. In this paper, we propose a deep autoencoder driven streaming methods to learn low-dimensional representations for graphs. The proposed method process the graph as a data stream fulfilled by sampling strategy to avoid straight computation over the large adjacent matrix. Moreover, a graph regularized deep autoencoder is employed in the model to keep different aspects of proximity information. The regularized framework is able to improve the representation power of learned features during the learning process. We evaluate our method in clustering task by the features learned from our model. Experiments show that the proposed method achieves competitive results comparing with methods that directly apply deep models over the complete graphs.

关键词： Streaming methods Representation learning deep autoencoder Graph regularization

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GTDNN-Based Voice Conversion Using DAEs with Binary Distributed Hidden Units 11

GTDNN-Based Voice Conversion Using DAEs with Binary Distribu...

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11th International Symposium on Chinese Spoken Language Processing (ISCSLP)

作者： Ding, Yi-Yang Hu, Ya-Jun Ling, Zhen-Hua Univ Sci & Technol China Natl Engn Lab Speech & Language Informat Proc Hefei Anhui Peoples R China

ISBN: (纸本)9781538656273

This paper proposes a method that adopts deep autoencoders with binary distributed hidden units (BDAE) as feature extractors in generatively trained DNNs (GTDNN) for voice conversion (VC). In this method, the source and target speakers are modeled separately by two BDAEs, and the extracted high-level features are used to train a Bernoulli bidirectional associated memory network (BBAM) for feature mapping. Then, the estimated model parameters are copied to construct a DNN for voice conversion. Compared with other neural network-based features extractors, such as deep belief networks (DBN) and conventional deep autoencoders (DAE), BDAEs can obtain a better balance between reconstruction error and the degree of binarization of hidden units. Our experimental results show that the GTDNN using BDAEs achieved better naturalness and similarity of converted speech than the ones using DBNs and conventional DAEs. Further experiments show the BDAE model for target speaker played a more important role than the one for source speaker.

关键词： voice conversion deep neural network deep autoencoder feature extraction

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Combining Satellite Images with Feature Indices for Improved Change Detection 9

Combining Satellite Images with Feature Indices for Improved...

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9th Annual IEEE Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON)

作者： Perez, Daniel Lu, Yan Kwan, Chiman Shen, Yuzhong Koperski, Krzysztof Li, Jiang Old Dominion Univ Dept Modeling Simulat & Visualizat Engn Norfolk VA 23529 USA Old Dominion Univ Dept Elect & Comp Engn Norfolk VA USA Appl Res LLC Rockville MD USA Digital Globe Inc Westminster CO USA

ISBN: (纸本)9781538676936

This paper presents a novel approach to detect changes in satellite images taken from the same location at different timestamps. Different change detection methods are applied to multispectral satellite images taken with the Worldview-2 (WV-2) satellite, as well as to several of their feature indices such as normalized difference vegetation index (NDVI), normalized difference soil index (NDSI), non-homogeneous feature index (NHFD) and red-blue ratio (R/B). Besides, an additional image is used to remove temporary changes like vehicles, persons etc. The combination of changes is computed with a set of pixel-wise operations, and morphological filters are applied to improve the final change map. The combination of the satellite images with their feature indices proved to produce better results than computing the changes independently. This paper summarizes the methodology and presents the results obtained.

关键词： Worldview-2 images change detection deep autoencoder remote sensing

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deep Optimisation: Multi-scale Evolution by Inducing and Searching in deep Representations 24th

Deep Optimisation: Multi-scale Evolution by Inducing and Sea...

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24th International Conference on the Applications of Evolutionary Computation (EvoApplications) Held as Part of EvoStar Conference

作者： Caldwell, Jamie Knowles, Joshua Thies, Christoph Kubacki, Filip Watson, Richard Univ Southampton Agents Interact & Complex AIC Res Grp Southampton SO17 1BJ Hants England Invenia Labs Cambridge CB2 1AW England

ISBN: (纸本)9783030726980;9783030726997

The ability of evolutionary processes to innovate and scale up over long periods of time, observed in nature, remains a central mystery in evolutionary biology, and a challenge for algorithm designers to emulate and explain in evolutionary computation (EC). The Major Transitions in Evolution is a compelling theory that explains evolvability through a multi-scale process whereby individuality (and hence selection and variation) is continually revised by the formation of associations between formerly independent entities, a process still not fully explored in EC. deep Optimisation (DO) is a new type of model-building optimization algorithm (MBOA) that exploits deep learning methods to enable multi-scale optimization. DO uses an autoencoder model to induce a multi-level representation of solutions, capturing the relationships between the lower-level units that contribute to the quality of a solution. Variation and selection are then performed within the induced representations, causing model-informed changes to multiple solution variables simultaneously. Here, we first show that DO has impressive performance compared with other leading MBOAs (and other rival methods) on multiple knapsack problems, a standard combinatorial optimization problem of general interest. Going deeper, we then carry out a detailed investigation to understand the differences between DO and other MBOAs, identifying key problem characteristics where other MBOAs are afflicted by exponential running times, and DO is not. This study serves to concretize our understanding of the Major Transitions theory, and why that leads to evolvability, and also provides a strong motivation for further investigation of deep learning methods in optimization.

关键词： Model-Building Optimisation Algorithms deep autoencoder Multi-scale search Problem structure

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deep Heterogeneous autoencoders for Collaborative Filtering 18

Deep Heterogeneous Autoencoders for Collaborative Filtering

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18th IEEE International Conference on Data Mining Workshops (ICDMW)

作者： Li, Tianyu Ma, Yukun Xu, Jiu Stenger, Bjorn Liu, Chen Hirate, Yu Rakuten Inst Technol Tokyo Japan Nanyang Technol Univ Singapore Singapore

ISBN: (纸本)9781538691595

This paper leverages heterogeneous auxiliary information to address the data sparsity problem of recommender systems. We propose a model that learns a shared feature space from heterogeneous data, such as item descriptions, product tags and online purchase history, to obtain better predictions. Our model consists of autoencoders, not only for numerical and categorical data, but also for sequential data, which enables capturing user tastes, item characteristics and the recent dynamics of user preference. We learn the autoencoder architecture for each data source independently in order to better model their statistical properties. Our evaluation on two MovieLens datasets and an e-commerce dataset shows that mean average precision and recall improve over state-of-the-art methods.

关键词： deep autoencoder Heterogeneous Data Shared Representation Sequential Data Modeling Collaborative Filtering

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Audio/Speech Coding Based on the Perceptual Sparse Representation of the Signal with DAE Neural Network Quantizer and Near-End Listening Enhancement 11th

Audio/Speech Coding Based on the Perceptual Sparse Represent...

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11th International Conference on Multimedia and Network Information Systems (MISSI)

作者： Herasimovich, Vadzim Petrovsky, Alexey Avramov, Vladislav Petrovsky, Alexander Belarusian State Univ Informat & Radioelect Minsk BELARUS

ISBN: (纸本)9783319986784;9783319986777

The article presents universal sound coding framework. The encoding algorithm works at the junction of the transform and parametric approaches. The input signal goes through the decorrelation transform - wavelet packet decomposition (WPD) that is tuned to perceptual structure of the analyzed signal with the psychoacoustic modelling. The parameterization stage is the matching pursuit (MP) algorithm with the WPD based dictionaries. Selected parameters then quantized and coded for the transmission to the decoder. Quantization algorithm based on the artificial neural networks with a deep autoencoder (DAE) architecture is presented. The decoding part of the coder has the listening enhancement function. Since the decoder input is the parameters that are distributed in the subbands it is only necessary to decompose the noise signal with the corresponding filterbank and estimate the subband gain factor based on this information. The results of the conducted research like objective difference grade and performance demonstration are shown.

关键词： Audio/speech coding Wavelet packet Matching pursuit Psychoacoustics Neural networks deep autoencoder Listening enhancement

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