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DDoS Attack Detection Based on Random Forest

DDoS Attack Detection Based on Random Forest

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symposium of image, signal processing, and Artificial Vision (STSIVA)

作者： Yini Chen Jun Hou Qianmu Li Huaqiu Long Nanjing University of Science and Technology Nanjing China Nanjing Vocational University of Industry Technology Nanjing China Wuyi University Jiangmen China

ISBN: (数字)9781728170862

ISBN: (纸本)9781728170879

With the development of network technology, distributed denial of service (DDoS) attacks have increasingly become an important security risk that endangers the network. It uses common protocols and services when attacking, so it is difficult to detect through traditional methods. Based on the idea of rational thinking, DDoS attack detection can be simulated as a classification problem that distinguishes between "rational" and "irrational" network flow states. This article analyzes the common TCP flood attacks, UDP flood attacks, and ICMP flood attacks in detail. Define the characteristics of data stream information entropy (DSIE) to characterize attack behavior. A DDoS attack detection method based on random forest classification (RFC) model is proposed. Establish classification models for the above three types of typical attack methods. Through training and learning, it is finally predicted whether the network traffic is normal. Experimental results show that the RFC model can more accurately distinguish between normal traffic and attack traffic, with a higher detection rate and a lower false alarm rate.

关键词： computational modeling Hidden Markov models Denial-of-service attack Floods Computer crime Information entropy Random forests

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image Regularization with Morphological Gradient Priors Using Optimal Structuring Elements for Each Pixel

Image Regularization with Morphological Gradient Priors Usin...

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ieee International symposium on Intelligent signal processing and Communication Systems (ISPACS)

作者： Shoya Oohara Hirotaka Oka Mitsuji Muneyasu Soh Yoshida Makoto Nakashizuka Faculty of Engineering Science Kansai University 3-3-35 Yamate-cho Suita-shi Osaka Japan Faculty of Engineering Chiba Institute of Technology 2-17-1 Tsudanuma Narashino Chiba Japan

As an image prior for image restoration, the use of the sum of the morphological gradient for an image has been proposed. In this paper, we propose a method of optimizing the structuring element (SE) for each pixel, in particular, by greatly reducing the computational burden for optimization by adopting a new evaluation method for the SE in simulated annealing. By optimizing the SE for each pixel, edges of the image can be faithfully evaluated, and the improvement of restoration accuracy can be expected. An experimental result shows the effectiveness of the proposed method.

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Fast Lip Feature Extraction Using Psychologically Motivated Gabor Features 8

Fast Lip Feature Extraction Using Psychologically Motivated ...

引用

8th ieee symposium Series on computational intelligence (ieee SSCI)

作者： Abel, Andrew Gao, Chengxiang Smith, Leslie Watt, Roger Hussain, Amir Xian Jiaotong Liverpool Univ Comp Sci & Software Engn Suzhou Peoples R China Univ Stirling Fac Nat Sci Stirling Scotland

ISBN: (纸本)9781538692769

The extraction of relevant lip features is of continuing interest in the speech domain. Using end-to-end feature extraction can produce good results, but at the cast of the results being difficult for humans to comprehend and relate to. We present a new, lightweight feature extraction approach, motivated by glimpse based psychological research into facial barcodes. This allows for 3D geometric features to be produced using Gabor based image patches. This new approach can successfully extract lip features with a minimum of processing, with parameters that can be quickly adapted and used for detailed analysis, and with preliminary results showing successful feature extraction from a range of different speakers. These features can be generated online without the need for trained models, and are also robust and can recover from errors, making them suitable for real world speech analysis.

关键词： image processing barcodes gabor lip-reading word features

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An Improved Watershed Algorithm Based on k-Medoids in Cervical Cancer images

An Improved Watershed Algorithm Based on k-Medoids in Cervic...

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ieee International symposium on Parallel and Distributed processing with Applications and ieee International Conference on Ubiquitous Computing and Communications (ISPA/IUCC)

作者： Bin Dong Linan Jia Yuntao Wang Jianqun Li Guojie Yang The Affiliated Hospital of Hebei University Baoding China Key Laboratory of Digital Medical Engineering of Hebei Province College of Electronic and Information Engineering Hebei University Baoding China

ISBN: (纸本)9781728152103

The watershed algorithm is widely used in the field of the image segmentation, which can overcome the difficulty of image analysis caused by cell overlap. However, the result of the image segmentation with the watershed algorithm were often over-segmentated. To solve this problem, the k-medoids clustering algorithm was introduced to simplify the gradient image, which is preprocessed from the original image. The edge information of the original image was obtained by the Canny edge detection operator, and the target region template was calculated by the optimized initial segmentation. Then, the segmentation result was obtained. The improved algorithm was evaluated by the segmentation accuracy compared with the professional segmented pathology image. The results show that the improved watershed algorithm proposed in this paper has a specific advantage in alleviating the phenomenon of over-segmentation, and the target area appears more completely.

关键词： cancer edge detection image segmentation medical image processing pattern clustering image segmentation Medical image processing Edge detection Cluster Analysis watershed watersheds original image algorithms images

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Study of unsaturated magnetic image processing in wire rope aplication

Study of unsaturated magnetic image processing in wire rope ...

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作者： Tan, Xiaojiang Zhang, Juwei Henan University of Science and Technology College of Electrical Engineering Luoyang China

ISBN: (纸本)9781538636749

Traditional magnetic flux leakage signal need be processed through each channels in magnetic array data, that increased processing system complexity. This paper proposed a processing system for unsaturated magnetic image, the processes included image transformation, filtering and resolution enhancement. This system overcame the shortcomings of interpolation method and digital signal processing. The experimental results showed that every defects could effectively be extracted and a good reconstruction image of multi-frame super-resolution was obtained with 3 simulated sampling intervals. © 2017 ieee.

关键词： image processing Magnetic imageing Super-resolution Wire rope

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DEEP FEATURE COMPRESSION FOR COLLABORATIVE OBJECT DETECTION 25

DEEP FEATURE COMPRESSION FOR COLLABORATIVE OBJECT DETECTION

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25th ieee International Conference on image processing (ICIP)

作者： Choi, Hyomin Bajic, Ivan V. Simon Fraser Univ Sch Engn Sci Burnaby BC Canada

ISBN: (纸本)9781479970612

Recent studies have shown that the efficiency of deep neural networks in mobile applications can be significantly improved by distributing the computational workload between the mobile device and the cloud. This paradigm, termed collaborative intelligence, involves communicating feature data between the mobile and the cloud. The efficiency of such approach can be further improved by lossy compression of feature data, which has not been examined to date. In this work we focus on collaborative object detection and study the impact of both near-lossless and lossy compression of feature data on its accuracy. We also propose a strategy for improving the accuracy under lossy feature compression. Experiments indicate that using this strategy, the communication overhead can be reduced by up to 70% without sacrificing accuracy.

关键词： Deep feature compression collaborative intelligence compression-augmentation object detection

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Defects Detection of Catenary Suspension Device Based on image processing and CNN

Defects Detection of Catenary Suspension Device Based on Ima...

引用

ieee symposium Series on computational intelligence (SSCI)

作者： Yongguang Li Dehua Wei Xiukun Wei Kuan Wu Yong Liang Xing Shen Shengzhou Tongzhen Technology Company Beijing China School of Traffic and Transportation Beijing Jiaotong University Beijing China State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing China CRRC Changchun Rail Transit Bus Co. Ltd Changchun China

ISBN: (数字)9781728124858

ISBN: (纸本)9781728124865

Catenary suspension device (CSD) is a critical part of the pantograph-catenary system, which is a fundamental power equipment to supply electricity to urban rail transit vehicles. Whether the CSD is in a normal state is of great significance to the safe operation of operating vehicles. Therefore, it is important to detect the defects of CSD in time and automatically. In this paper, innovative and intelligent methods using image processing technologies and convolutional neural network (CNN) are proposed. Firstly, the insulators and bolts of CSD are extracted in the detected images using template matching algorithm. After that, an improved Bag of Features (BOF) model is proposed for the defect detection of CSD. Furthermore, in order to further improve the detection efficiency, AlexNet is trained for the defect detection and identification of CSD. The experimental results show that the proposed methods can detect the defects of CSD in time, with high robustness and accuracy.

关键词： Convolutional neural networks image classification Feature extraction image matching Railway safety Fault detection

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An Enhanced SqueezeNet Based Network for Real-Time Road-Object Segmentation

An Enhanced SqueezeNet Based Network for Real-Time Road-Obje...

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ieee symposium Series on computational intelligence (SSCI)

作者： Chao Li Xian Wei Hui Yu Jielong Guo Xuan Tang Yuxuan Zhang Chinese Academy of Sciences Quanzhou Institute of Equipment Manufacturing Haixi Institutes Quanzhou China Chinese Academy of Science Fujian Institute of Research on the Structure of Matter Qaunzhou China Shanghai Jiao Tong University School of Aeronautics and Astronautics Shanghai China

ISBN: (数字)9781728124858

ISBN: (纸本)9781728124865

Point cloud image segmentation plays an important role in self-driving. SqueezeSeg network has good performance in terms of accuracy and calculation speed on point cloud segmentation. However, potential details might be lost during the computational processing of SqueezeSeg and other similar kinds of networks. In this work, we try to retain the detailed information of the image by combining PointSeg network and the conditional random field in order to capture more data information and improve the recall rate. These two processes can complement and fully play their respective advantages. The proposed method has been tested on KITTI dataset. Simulation results demonstrate that our method can overcome the shortcomings of the SqueezeSeg network and similar kinds of networks on the extraction of detailed information.

关键词： Three-dimensional displays image segmentation Kernel Semantics Convolution Sensors Fires

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Improved Fast Stereo Matching Algorithm Based on Convolutional Neural Networks

Improved Fast Stereo Matching Algorithm Based on Convolution...

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ieee International symposium on Parallel and Distributed processing with Applications and ieee International Conference on Ubiquitous Computing and Communications (ISPA/IUCC)

作者： Chunfeng Xu Shiyu Lu Wen Liu Ye Bai Cheng Han School of Computer Science and Technology Changchun University of Science and Technology Changchun China College of Optical and Electronical Information Changchun University of Science and Technology Changchun China

ISBN: (纸本)9781728152103

Various stereo matching algorithms are widely used in many fields nowadays. In order to solve low speed problem in convolutional neural network for stereo matching algorithms (MC-CNN-fst), an improved stereo matching algorithm based on convolutional neural network was proposed. By modifying pooling layers and normalization layer, the structure of the convolutional neural network has been reformed. Among them, different pooling layers are introduced to retain more image information, and the normalization layer is introduced to accelerate the speed of convergence of training, and finally the disparity map is obtained through subsequent processing steps. To evaluate the performance of the proposed algorithm and the original one under the same experimental environment. We conduct the experiment on two public datasets: KITTI and Middlebury. Experimental results demonstrate that the refinement of the network structure boosts the speed for the feature extraction procedure by 50%, and the total operation time is reduced by 12.5%.

关键词： convolutional neural nets feature extraction image matching stereo image processing Stereo image processing Feature extraction image matching Neural network operation time algorithms image information normalization NETWORK STRUCTURE

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Edge saliency based on Biological inspiration for object edge extraction under low illumination 11

Edge saliency based on Biological inspiration for object edg...

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11th International symposium on computational intelligence and Design (ISCID)

作者： Deng, Qicao Zhang, Liping Dou, Yiwen Wu, Jincenzi Liu, Tao AnHui Polytechn Univ Sch Mech & Automot Engn Wuhu Peoples R China AnHui Polytechn Univ Sch Comp & Informat Wuhu Peoples R China Deakin Univ Fac Sci Engn & Built Environm Geelong Vic Australia

ISBN: (纸本)9781538685273

Objects edge extraction under low illumination image is helpful to enhance the scotopic vision of human visual system. At present, the traditional edge detection algorithm can not find the important edge information under the low illumination image directly. Due to tackle this problem, an optimized the visual saliency model by using the biological heuristic intelligent algorithm is proposed. It is convenient to obtain edge information of low illuminance image. First of all, after obtaining low illumination image, the visual focus is extracted from optimized visual saliency model at three channel and multi-scale level. Then those edge saliency focuses are determined by normalization and merging strategy. In this way, it lays the foundation for the posterior procedures of visual processing. Experimental results show that the proposed algorithm can accurately locate and extract the edge of the objects under normal illumination and low illumination, and it also has the characteristics of high noise resistance.

关键词： Low Illumination image Edge Saliency Firefly Population Entropy

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