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An Effective Deep Learning Based Scheme for Network Intrusion Detection 24

An Effective Deep Learning Based Scheme for Network Intrusio...

引用

24th International Conference on Pattern Recognition (ICPR)

作者： Zhang, Hongpo Wu, Chase Q. Gao, Shan Wang, Zongmin Xu, Yuxiao Liu, Yongpeng State Key Lab Math Engn & Adv Comp Zhengzhou Henan Peoples R China Zhengzhou Univ Cooperat Innovat Ctr Internet Healthcare Zhengzhou Henan Peoples R China New Jersey Inst Technol Dept Comp Sci Newark NJ 07102 USA Hangzhou DPtech Technol Co Ltd Dept Res & Dev Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9781538637883

Intrusion detection systems (IDS) play an important role in the protection of network operations and services. In this paper, we propose an effective network intrusion detection scheme based on deep learning techniques. The proposed scheme employs a denoising autoencoder (DAE) with a weighted loss function for feature selection, which determines a limited number of important features for intrusion detection to reduce feature dimensionality. The selected data is then classified by a compact multilayer perceptron (MLP) for intrusion identification. Extensive experiments are conducted on the UNSW-NB dataset to demonstrate the effectiveness of the proposed scheme. With a small feature selection ratio of 5.9%, the proposed scheme is still able to achieve a superior performance in terms of different evaluation criteria. The strategic selection of a reduced set of features yields satisfactory detection performance with low memory and computing power requirements, making the proposed scheme a promising solution to intrusion detection in high-speed networks.

关键词： Intrusion detection system denoising autoencoder feature selection deep learning

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Reducing Speckle Noise from Ultrasound Images Using an autoencoder Network 28

Reducing Speckle Noise from Ultrasound Images Using an Autoe...

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28th Signal Processing and Communications Applications Conference (SIU)

作者： Karaoglu, Onur Bilge, Hasan Sakir Uluer, Ihsan Karabuk Univ Elekt Elekt Muhendisligi Karabuk Turkey Gazi Univ Elekt Elekt Muhendisligi Ankara Turkey

ISBN: (纸本)9781728172064

Image enhancement aims to obtain a clear image from a noisy image and it also uses for ultrasound images. In the experimental study, unlike classical image enhancement methods, deep learning method was used. Different levels of speckle noise added to the ultrasound images of the brachial plexus, which is known as the large nerve community under the armpit, were tried to be removed with the help of the convolutional denoising autoencoder network, which is one of the deep learning methods. The results obtained from the experimental study were compared with classical methods results and the proposed method was found to be more successful than classical methods.

关键词： image enhancement deep learning ultrasound imaging denoising autoencoder

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Bottleneck Features from SNR-Adaptive denoising Deep Classifier for Speaker Identification

Bottleneck Features from SNR-Adaptive Denoising Deep Classif...

引用

Asia-Pacific-Signal-and-Information-Processing-Association Annual Summit and Conference (APSIPA ASC)

作者： Tan, Zhili Mak, Man-Wai Hong Kong Polytech Univ Dept Elect & Informat Engn Ctr Signal Proc Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9789881476807

In this paper, we explore the potential of using deep learning for extracting speaker-dependent features for noise robust speaker identification. More specifically, an SNR-adaptive denoising classifier is constructed by stacking two layers of restricted Boltzmann machines (RBMs) on top of a denoising deep autoencoder, where the top-RBM layer is connected to a soft-max output layer that outputs the posterior probabilities of speakers and the top-RBM layer outputs speaker-dependent bottleneck features. Both the deep autoencoder and RBMs are trained by contrastive divergence, followed by backpropagation fine-tuning. The autoencoder aims to reconstruct the clean spectra of a noisy test utterance using the spectra of the noisy test utterance and its SNR as input. With this denoising capability, the output from the bottleneck layer of the classifier can be considered as a low-dimension representation of denoised utterances. These frame-based bottleneck features are than used to train an iVector extractor and a PLDA model for speaker identification. Experimental results based on a noisy YOHO corpus show that the bottleneck features slightly outperform the conventional MFCC under low SNR conditions and that fusion of the two features lead to further performance gain, suggesting that the two features are complementary with each other.

关键词： Deep learning Bottleneck features denoising autoencoder speaker identification deep belief networks

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Speech Enhancement Based on Analysis Synthesis Framework With Improved Pitch Estimation and Spectral Envelope Enhancement 12

Speech Enhancement Based on Analysis Synthesis Framework Wit...

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12th IEEE International Conference on Signal Processing (ICSP)

作者： Liu, Bin Mo, Fuyuan Tao, Jianhua Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Beijing 100190 Peoples R China Chinese Acad Sci Inst Acoust Beijing 100190 Peoples R China

ISBN: (纸本)9781479921867

This paper presents a speech enhancement approach based on analysis synthesis framework An improved multi-band summary correlogram (MBSC) algorithm is proposed for pitch estimation and voiced/unvoiced (V/UV) detection. The proposed pitch detection algorithm achieves a lower pitch detection error compared with the reference algorithm. The denoising autoencoder (DAE) is applied to enhance the line spectrum frequencies (LSFs). The reconstruction loss could be decreased compare with the swallow model. The proposed approach is evaluated using the perceptual evaluation of speech quality (PESQ) and the experimental results show that the proposed approach improves the performance of speech enhancement compared with the conventional speech enhancement approach. In addition, it could be applied to parametric speech coding even at low bit rate and low SNR environments.

关键词： analysis-synthesis framework multi-band summary correlogram denoising autoencoder speech enhancement speech coding

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A Low-complexity Visual Tracking Approach with Single Hidden Layer Neural Networks 13

A Low-complexity Visual Tracking Approach with Single Hidden...

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13th International Conference on Control Automation Robotics & Vision (ICARCV)

作者： Dai, Liang Zhu, Yuesheng Luo, Guibo He, Chao Peking Univ Shenzhen Grad Sch Inst Big Data Technol Lab Commun & Informat Secur Beijing Peoples R China

ISBN: (纸本)9781479951994

Visual tracking algorithms based on deep learning have robust performance against variations in a complex environment because deep learning can learn generic features from numerous unlabeled images. However, due to the multi-layer architecture, the deep learning trackers suffer from expensive computational costs and are not suitable for real-time applications. In this paper, a low-complexity visual tracking scheme with single hidden layer neural network is proposed based on denoising autoencoder. To further reduce the computational costs, feature selection is applied to simplify the networks and two optimization methods are used during the online tracking process. The experimental results have demonstrated that the proposed algorithm is about six times faster than the trackers based on deep nets and rapid enough for real-time applications with encouraging accuracy.

关键词： visual tracking neural network denoising autoencoder single hidden layer

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Frequency Offset Correction in Single Sideband(SSB) Speech by Deep Neural Network for Speaker Verification 16

Frequency Offset Correction in Single Sideband(SSB) Speech b...

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16th Annual Conference of the International-Speech-Communication-Association (INTERSPEECH 2015)

作者： Xing, Hua Liu, Gang Hansen, John H. L. Univ Texas Dallas Ctr Robust Speech Syst Richardson TX 75083 USA

ISBN: (纸本)9781510817906

Communication system mismatch represents a major influence for loss in speaker recognition performance. This paper considers a type of nonlinear communication system mismatch- modulation/demodulation (Mod/DeMod) carrier drift in single side band (SSB) speech signals. We focus on the problem of estimating frequency offset in SSB speech in order to improve speaker verification performance of the drifted speech. Based on a two-step framework from previous work, we propose using a multi-layered neural network architecture, stacked denoising autoencoder (SDA), to determine the unique interval of the offset value in the first step. Experimental results demonstrate that the SDA based system can produce up to a +16.1% relative improvement in frequency offset estimation accuracy. A speaker verification evaluation shows a +65.9% relative improvement in EER when SSB speech signal is compensated with the frequency offset value estimated by the proposed method.

关键词： frequency offset single sideband speaker verification denoising autoencoder

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A Lightweight sequence-based Unsupervised Loop Closure Detection

A Lightweight sequence-based Unsupervised Loop Closure Detec...

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International Joint Conference on Neural Networks (IJCNN)

作者： Xiong, Fan Ding, Yan Yu, Mingrui Zhao, Wenzhe Zheng, Nanning Ren, Pengju Xi An Jiao Tong Univ Coll Artificial Intelligence Xian Peoples R China

ISBN: (纸本)9780738133669

Stable, effective and lightweight loop closure detection is an always pursued goal in real-time SLAM systems, that can be ported on embedded processors and deployed on autonomous robotics. Deep learning methods have extended the expressive ability and adaptability of the descriptor, and sequence-based methods can greatly improve the matching accuracy. However, the increased computation complexity and storage bandwidth requirements of matching calculations for high-dimensional descriptor make it infeasible for real-time deployment, especially for robots that navigate in relatively big maps. To address this challenge, we propose a lightweight sequence-based unsupervised loop closure detection scheme. To be specific, Principal Component Analysis (PCA) is applied to squeeze the descriptor dimensions while maintaining sufficient expressive ability. Additionally, with the consideration of the image sequence and combining linear query with fast approximate nearest neighbor search to further reduce the execution time and improve the efficiency of sequence matching. We implement our method on CALC, a state-of-the-art unsupervised solution, and conduct experiments on NVIDIA TX2, results demonstrate that the accuracy has been improved by 5%, while the execution speed is 2x faster.

关键词： Simultaneous Localization and Mapping Loop Closure Detection PCA Images Sequence denoising autoencoder

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ROBUST RECOGNITION OF SPEECH WITH BACKGROUND MUSIC IN ACOUSTICALLY UNDER-RESOURCED SCENARIOS

ROBUST RECOGNITION OF SPEECH WITH BACKGROUND MUSIC IN ACOUST...

引用

IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Malek, Jiri Zdansky, Jindrich Cerva, Petr Tech Univ Liberec Fac Mechatron Informat & Interdisciplinary Studie Studentska 2 Liberec 46117 Czech Republic

ISBN: (纸本)9781538646588

This paper addresses the task of Automatic Speech Recognition (ASR) with music in the background. We consider two different situations: 1) scenarios with very small amount of labeled training utterances (duration 1 hour) and 2) scenarios with large amount of labeled training utterances (duration 132 hours). In these situations, we aim to achieve robust recognition. To this end we investigate the following techniques: a) multi-condition training of the acoustic model, b) denoising autoencoders for feature enhancement and c) joint training of both above mentioned techniques. We demonstrate that the considered methods can be successfully trained with the small amount of labeled acoustic data. We present substantially improved performance compared to acoustic models trained on clean speech. Further, we show a significant increase of accuracy in the under-resourced scenario, when utilizing additional amount of non-labeled data. Here, the non-labeled dataset is used to improve the accuracy of the feature enhancement via autoencoders. Subsequently, the autoencoders are jointly fine-tuned along with the acoustic model using the small amount of labeled utterances.

关键词： robust speech recognition feature enhancement denoising autoencoder multi-condition training joint training

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Drone Noise Reduction using Deep Convolutional autoencoder for UAV Acoustic Sensor Networks 16

Drone Noise Reduction using Deep Convolutional Autoencoder f...

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16th IEEE International Conference on Mobile Ad Hoc and Smart Systems (IEEE MASS)

作者： Chun, Chanjun Jeon, Kwang Myung Kim, Taewoon Choi, Wooyeol Korea Inst Civil Engn & Bldg Technol Future Infrastruct Res Ctr Goyang 10223 South Korea IntFlow Co Ltd AI Convergence Technol Res Ctr Gwangju 61080 South Korea Hallym Univ Sch Software Chunchon 24252 South Korea Chonsun Univ Dept Comp Engn Gwangju 61452 South Korea

ISBN: (纸本)9781728141213

Drones are widely utilized in various industries. Unfortunately, when a drone acquires sound through a microphone, which is installed itself, drone Hying and wind noises appear in recorded signals. Therefore, it is necessary to reduce such drone flying and wind noises for enhancing the quality of the recorded sound signals for UAV acoustic sensor networks. In this paper, we propose the noise reduction method using a deep convolutional demising autoencoder for eliminating drone flying and wind noises. The deep convolutional denoising autoencoder is widely utilized to extract the target sound source in monaural audio source separation. To do this task, a training dataset is constructed by mixing drone flying and wind noises in clean speech signal. Also, we train the neural network model, which is in form of fully convolutional neural networks. From the sound signals recorded in the real outdoor environment, it is shown that the trained model can reduce the drone flying and wind noises, and only separate the target speech.

关键词： Drone noise noise reduction convolutional neural network denoising autoencoder acoustic sensor network

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Author Identification using Deep Learning 15

Author Identification using Deep Learning

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15th IEEE International Conference on Machine Learning and Applications (ICMLA)

作者： Mohsen, Ahmed M. El-Makky, Nagwa M. Ghanem, Nagia Alexandria Univ Fac Engn Alexandria Egypt

ISBN: (纸本)9781509061679

Authorship identification is the task of identifying the author of a given text from a set of suspects. The main concern of this task is to define an appropriate characterization of texts that captures the writing style of authors. Although deep learning was recently used in different natural language processing tasks, it has not been used in author identification (to the best of our knowledge). In this paper, deep learning is used for feature extraction of documents represented using variable size character n-grams. We apply A Stacked denoising autoencoder (SDAE) for extracting document features with different settings, and then a support vector machine classifier is used for classification. The results show that the proposed system outperforms its counterparts

关键词： denoising autoencoder author identification deep learning

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