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检索条件"任意字段=International Conference on Signal and Data Processing, ICSDP 2019"

共 2690 条记录，以下是131-140 订阅

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Enhanced SDN Defense: Analysis of DDoS Attack Detection Techniques Using Machine Learning

Enhanced SDN Defense: Analysis of DDoS Attack Detection Tech...

引用

IEEE international conference on signal processing, Information, Communication & Systems (SPICSCON)

作者： Diganta Majumder Monir Hossen Sujit Basu Electronics and Communication Engineering Khulna University of Engineering & Technology Khulna Bangladesh Institute of Information and Communication Technology Khulna University of Engineering & Technology Khulna Bangladesh

ISBN: (数字)9798331510213

ISBN: (纸本)9798331510220

Software defined network (SDN) is drawing attention to handling next-generation traffic through centralized management. However, the centralized aspect of the network makes it more vulnerable because the central controller might be targeted by malicious actors looking to interfere with network operations and data security. Typically, the SDN controller defense system uses machine learning (ML) techniques to inspect data from individual IP to detect distributed denial-of-service and intrusion attacks. As soon as an anomaly is detected, the system enables rapid response to mitigate attacks and ensure smooth system operations. In the accessed model, the CICDDoS-2019 public dataset is used to evaluate and compare the performance with various ML algorithms. Ultimately, an efficient mitigation approach is suggested and utilized for each detection method. The results suggest guaranteed detection rates and efficient throughput. Therefore, the multi-layer perceptron approach is suitable for the SDN defense system.

关键词： Machine learning algorithms Systems operation Prevention and mitigation signal processing algorithms Machine learning signal processing Denial-of-service attack Throughput Software defined networking Next generation networking

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ALLOCATING DNN LAYERS COMPUTATION BETWEEN FRONT-END DEVICES AND THE CLOUD SERVER FOR VIDEO BIG data processing

ALLOCATING DNN LAYERS COMPUTATION BETWEEN FRONT-END DEVICES ...

引用

IEEE international conference on Acoustics, Speech and signal processing (ICASSP)

作者： Xing, Peiyin Liu, Xiaofei Peng, Peixi Huang, Tiejun Tian, Yonghong Peking Univ Sch EECS Beijing Peoples R China Pengcheng Lab Shenzhen Peoples R China

ISBN: (纸本)9781728176055

With the development of intelligent hardware, front-end devices can also perform DNN computation. Moreover, the deep neural network can be divided into several layers. In this way, part of the computation of DNN models can be migrated to the front-end devices, which can alleviate the cloud burden and shorten the processing latency. This paper proposes a computation allocation algorithm of DNN between the front-end devices and the cloud server. In brief, we divide the DNN layers dynamically according to the current and the predicted future status of the processing system, by which we obtain a shorter end-to-end latency. The simulation results reveal that the overall latency reduction is more than 70% compared with traditional cloud-centered processing.

关键词： Collaborative processing Video Big data Edge Computing Intelligent Front-end DNN

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Optimal Reliability Density Function Based on Entropy Index Under Single Parameter and Two Parameters 1

引用

17th international conference on Intelligent Information Hiding and Multimedia signal processing (IIH-MSP) / 14th international conference on Frontiers of Information Technology, Applications and Tools (FITAT)

作者： Xie, Chao-Fan Zhang, De-Bao Zhang, Fuquan Fujian Polytech Normal Univ Sch Big Data & Artificial Intelligence Fuzhou Fujian Peoples R China FuJian FuQing LongHua Vocat Tech Sch Dept Informat Technol Fuzhou Fujian Peoples R China Minjiang Univ Fujian Prov Key Lab Informat Proc & Intelligent C Fuzhou Peoples R China

ISBN: (数字)9789811910579

ISBN: (纸本)9789811910579;9789811910562

As an important branch of mathematical statistics, reliability science provides a solid theoretical basis for the field of electrical engineering and aerospace. Different from the research methods of previous scholars, they mainly propose the reliability and failure rate indexes based on the probability density function of the system and optimize them. In this paper, the stability indexes based on thermodynamic entropy are optimized, and the variational method is used as a research tool to find the optimal reliability curve in the single parameter and two parameter probability density space.

关键词： Optimize Reliability Variation

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Generalizable Audio Deepfake Detection via Latent Space Refinement and Augmentation

Generalizable Audio Deepfake Detection via Latent Space Refi...

引用

international conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Wen Huang Yanmei Gu Zhiming Wang Huijia Zhu Yanmin Qian AI Institute Department of Computer Science and Engineering Auditory Cognition and Computational Acoustics Lab MoE Key Lab of Artificial Intelligence Shanghai Jiao Tong University Shanghai China SJTU Paris Elite Institute of Technology Ant Group Shanghai China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Advances in speech synthesis technologies, like text-to-speech (TTS) and voice conversion (VC), have made detecting deepfake speech increasingly challenging. Spoofing countermeasures often struggle to generalize effectively, particularly when faced with unseen attacks. To address this, we propose a novel strategy that integrates Latent Space Refinement (LSR) and Latent Space Augmentation (LSA) to improve the generalization of deepfake detection systems. LSR introduces multiple learnable prototypes for the spoof class, refining the latent space to better capture the intricate variations within spoofed data. LSA further diversifies spoofed data representations by applying augmentation techniques directly in the latent space, enabling the model to learn a broader range of spoofing patterns. We evaluated our approach on four representative datasets, i.e. ASVspoof 2019 LA, ASVspoof 2021 LA and DF, and In-The-Wild. The results show that LSR and LSA perform well individually, and their integration achieves competitive results, matching or surpassing current state-of-the-art methods.

关键词： Deepfakes Refining Prototypes data visualization signal processing Robustness data models Acoustics Text to speech

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A Correlation-Based Local Differential Privacy Scheme Design for Trajectory data Collection 5

A Correlation-Based Local Differential Privacy Scheme Design...

引用

5th international conference on Information Communication and signal processing, ICICSP 2022

作者： Tian, Jingxuan Chen, Hong Qiu, Zhijie Luo, Lei Zhang, Peichang College of Scse University of Electronic Science and Technology of China ChengDu China College of Electronic and Information Engineering ShenZhen Uni ShenZhen China

ISBN: (纸本)9781665485890

In recent years, new industrial forms has continuously been generated with the rapid development of big data technology. However, the problem of data privacy and security has become increasingly prominent. In numerous geographically related applications, location trajectory data which involve sen- sitive personal privacy need more stringent protection measures. Thus a method has been proposed before that combining hidden Markov model with local differential privacy mechanism, for training and generating a biased trajectory dataset. Nevertheless, this method did not take into account the spatial correlation between track coordinates, which causes a large deviation be- tween the disturbed trajectory and the real trajectory. Aiming at this problem, this paper brings forward a (\varepsilon,\delta) -local differential privacy mechanism based on trajectory coordinate correlation. On the basis of existing schemes, it emphasizes the spatial correlation of trajectory coordinates. Furthermore, it may ensure the availability of track data by reasonably setting the dynamic parameter of δ. Finally, we conduct experiments on data sets of Taxi trajectory prediction. The results show that our Correlation Multivariate Random Response (CMRR) algorithm presents significantly better performance than Generalized Randomized Response (GRR) in data availability. In the meanwhile, it strictly meets the privacy protection requirements. © 2022 IEEE.

关键词： data privacy

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data-Based Solar Radiation Forecasting with Pre-processing Using Variational Mode Decomposition 9

Data-Based Solar Radiation Forecasting with Pre-Processing U...

引用

9th international conference on Control, Decision and Information Technologies, CoDIT 2023

作者： El Bakali, Saida Ouadi, Hamid Giri, Fouad Gheouany, Saad El-Bakkouri, J. Erera Ensam Mohammed v University Rabat Morocco Normandie University Unicaen Ensicaen Lac Caen14000 France Lese Ensem Hassan Ii University Casablanca Morocco

ISBN: (纸本)9798350311402

This paper presents a hybrid method for accurately predicting Global Horizontal Irradiance (GHI) over the following 24 hours to forecast energy production from a photo-voltaic system in a positive energy building. The input data is preprocessed using the Variational Mode Decomposition (VMD) method to extract wide-bandwidth features and decompose them into smooth modes focused on specific frequency ranges. The Salp Swarm Algorithm (SSA) is utilized to identify the optimal VMD parameters for accurate extraction. The data analysis is employed to identify the most critical modes of input features. The model's efficiency is further enhanced by performing a residual preprocessing step between the observed solar radiance data and the decomposed modes. The Stacking technique (ST) is employed to predict the 24-hour GHI modes and the residual, which are summed to reconstruct the final signal. The proposed method's performance is evaluated using the Normalized Root Mean Square Error (NRMSE) and Normalized Mean Absolute Error (NMAE) metrics on three years of available data (2019-2022) in Rabat, and compared with the model based on raw data. The results show that the proposed method achieved promising results with an NRMSE of 1.35% and NMAE of 0.82% on a cloudy day. © 2023 IEEE.

关键词： Variational mode decomposition

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An Initial Investigation of Neural Replay Simulator for Over-The-Air Adversarial Perturbations to Automatic Speaker Verification

An Initial Investigation of Neural Replay Simulator for Over...

引用

international conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Jiaqi Li Li Wang Liumeng Xue Lei Wang Zhizheng Wu School of Data Science Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen (CUHK-Shenzhen) China Independent Researcher Singapore

Deep Learning has advanced Automatic Speaker Verification (ASV) in the past few years. Although it is known that deep learning-based ASV systems are vulnerable to adversarial examples in digital access, there are few studies on adversarial attacks in the context of physical access, where a replay process (i.e., over the air) is involved. An over-the-air attack involves a loudspeaker, a microphone, and a replaying environment that impacts the movement of the sound wave. Our initial experiment confirms that the replay process impacts the effectiveness of the over-the-air attack performance. This study performs an initial investigation towards utilizing a neural replay simulator to improve over-the-air adversarial attack robustness. This is achieved by using a neural waveform synthesizer to simulate the replay process when estimating the adversarial perturbations. Experiments conducted on the ASVspoof2019 dataset confirm that the neural replay simulator can considerably increase the success rates of over-the-air adversarial attacks. This raises the concern for adversarial attacks on speaker verification in physical access applications.

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An ECG signal de-noising method combining the improved threshold function and ABC algorithm 3

An ECG signal de-noising method combining the improved thres...

引用

2022 3rd international conference on Computer Information and Big data Applications, CIBDA 2022

作者： Deng, Yanrong Huang, Yanhu School of Information and Communication Guilin University of Electronic Technology Guilin541004 China Guangxi Colleges and Universities Key Laboratory of Complex System Optimization and Big Data Processing Yulin Normal University Yulin537000 China Guangxi Key Laboratory of Precision Navigation Technology and Application Guilin University of Electronic Technology Guilin541004 China

ISBN: (纸本)9783800758760

Electrocardiogram (ECG) plays an imperative role in the clinical diagnosis of heart disease and heart abnormalities. In order to obtain the original ECG signal morphology, the wavelet threshold technique is usually used for ECG signal de-noising, in which threshold function and thresholds play very important role. In this paper, an improved threshold function with adjustable shape parameter was structured to improve the problems of the constant deviation in soft threshold function and the discontinuousness in hard threshold function. And then, an optimization strategy based on artificial bee colony algorithm with mean square error (MSE) as the objective function is embedded into the process of de-noising to quickly determine the optimal shape parameter and thresholds. Subsequently, a series of the simulation experiment are carried out by using two benchmark signals and real ECG signal with different noise levels. The signal-to-noise ratio (SNR) and the MSE after de-noising are used as evaluation index of the proposed method. The evaluation results demonstrate that the improved threshold function shows a better performance than the traditional threshold functions under different noise levels, and the proposed method is effective for the selections of the optimal shape parameter and thresholds in ECG signal de-noising. © VDE VERLAG GMBH - Berlin - Offenbach.

关键词： Optimization

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data AUGMENTATION WITH signal COMPANDING FOR DETECTION OF LOGICAL ACCESS ATTACKS

DATA AUGMENTATION WITH SIGNAL COMPANDING FOR DETECTION OF LO...

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IEEE international conference on Acoustics, Speech and signal processing (ICASSP)

作者： Das, Rohan Kumar Yang, Jichen Li, Haizhou Natl Univ Singapore Dept Elect & Comp Engn Singapore Singapore

ISBN: (纸本)9781728176055

The recent advances in voice conversion (VC) and text-to-speech (TTS) make it possible to produce natural sounding speech that poses threat to automatic speaker verification (ASV) systems. To this end, research on spoofing countermeasures has gained attention to protect ASV systems from such attacks. While the advanced spoofing countermeasures are able to detect known nature of spoofing attacks, they are not that effective under unknown attacks. In this work, we propose a novel data augmentation technique using a-law and mu-law based signal companding. We believe that the proposed method has an edge over traditional data augmentation by adding small perturbation or quantization noise. The studies are conducted on ASVspoof 2019 logical access corpus using light convolutional neural network based system. We find that the proposed data augmentation technique based on signal companding outperforms the state-of-the-art spoofing countermeasures showing ability to handle unknown nature of attacks.

关键词： data augmentation signal companding anti-spoofing synthetic speech detection

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Improved Federated Average Algorithm for Non-independent and Identically Distributed data 8th

Improved Federated Average Algorithm for Non-independent and...

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8th international conference on signal and Information processing, Network and Computers, ICSINC 2021

作者： Zhou, Zhongchang Sun, Fenggang Lan, Peng Wang, Zhijun College of Information Science and Engineering Shandong Agricultural University Tai’an271000 China

ISBN: (纸本)9789811933868

Federated learning is a distributed machine learning model that can protect data privacy, where a large number of clients and servers participate in the joint train procedure. However, the problem of heterogeneous data has been a potential obstacle for federated learning. Traditional joint averaging algorithms suffer from low accuracy and slow convergence on non-IID (identical and independently distributed) data. To this end, this paper proposes an improved FedAvg based algorithm called FedAvg-Z. We introduce penalty coefficients and adaptive parameter averaging methods in our algorithm, which adjusts the influence of clients on the global model as a way to increase the convergence and stability of the model. We first demonstrate the reliability of our method on the dataset and then compare it experimentally with the traditional algorithm. The experiments show that the algorithm has higher accuracy and convergence speed in the non-IID case. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Machine learning

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