2018 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)最新文献

{"title":"Neural Network Rule Extraction for Real Time Traffic Behavior Identification","authors":"Anli Yan, Zhenxiang Chen, Lin Wang, Lizhi Peng, Muhammad Umair Hassan, Chuan Zhao","doi":"10.1109/SPAC46244.2018.8965635","DOIUrl":"https://doi.org/10.1109/SPAC46244.2018.8965635","url":null,"abstract":"The rapid identification of network traffic classification has become an important network management tasks. Although the current research methods can classify the network traffic and achieve high accuracy. Because of the high complexity of computing, memory consumption and other reasons, they can only provide 100 megabytes of processing power and cannot handle a large number of concurrent traffic situation. Aiming at this problem, this paper proposes a method to identify the traffic classification on the network, and selects the early identified traffic characteristics as the attributes of the training model to alleviate the resource and time consumption of the online traffic classification. The method first sends the traffic data to the trained neural network and extracts the fuzzy traffic data. And then the fuzzy traffic data optimizes by genetic algorithm and sends to the decision tree algorithm to generate decision tree. Finally, the decision tree is transformed into rules, such as if-then rules. The resulting rules use FPGA as the application context to achieve online network traffic classification. This method is not only an innovation in the field of neural network extraction, but also a novel method of neural network rule extraction to solve the online network traffic classification.","PeriodicalId":360369,"journal":{"name":"2018 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)","volume":"28 7","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120905487","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Research and application of security video labeling platform","authors":"Yanfeng Zhang, Yi Wei, Liang Ma","doi":"10.1109/SPAC46244.2018.8965442","DOIUrl":"https://doi.org/10.1109/SPAC46244.2018.8965442","url":null,"abstract":"With the rapid development of science and technology, the intelligent security technology has developed rapidly. As far as the deep learning method used in the field of intelligent security is concerned, image marking is a heavy and complicated work. The current security video is usually manually completed, which increases the operation and maintenance cost of security video system. For this reason, this paper proposes a semi-automatic annotation method based on neighborhood rough set for the complexity of distorted image annotation. The division of theoretical domain and the definition of correlation degree are redone. The aim is to start with the manual annotation of the data source, improve the quality of the annotation, and solve the difficulty of the annotation caused by the standard deviation of the subjective evaluation of the annotator and the blurring of the boundary between the annotated words. The application example shows that the security video labeling platform can realize semiautomatic video labeling and significantly improve the accuracy rate of security video labeling.","PeriodicalId":360369,"journal":{"name":"2018 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115286526","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Anomaly Detection Based on Job Monitoring Metrics in Distributed System","authors":"Meixiang Ding, Zhi Xiong, Jian Yu","doi":"10.1109/SPAC46244.2018.8965641","DOIUrl":"https://doi.org/10.1109/SPAC46244.2018.8965641","url":null,"abstract":"In distributed systems, application delays caused by stragglers become a common problem. And interference by competing the resources can make more stragglers. Previous works mostly focus on straggler detection using statistical analysis methods based on the data extracted from logs. These methods cannot provide fine-grained insights to help users optimize their programs. In this paper, we propose an anomaly detection approach using classification method in machine learning based on job monitoring resource metrics. Due to interference, the change of metrics may vary randomly as the job progresses. In order to compare the metrics in different situation, we extract the job, stage and task information from the logs. From the point of system resource utilization, there are three kinds of anomalies we detect, which are the stragglers(tasks), the abnormal jobs and the interfered nodes. We prove that in most situation, more stragglers happen under interference, and the task time for defining stragglers is longer than that in the similar stage time, as well as the node that the abnormal jobs lived is the interfered nodes.We use the task time in the same stage to label the data for training the adaptive boosting classifier model solely with the resource features. In this way, the model can detect straggles, abnormal jobs and interfered nodes in real-time. Additionally, Experiments show that the accuracy of anomaly detection reaches 92%. Case studies show that our framework is effective in detecting abnormal jobs and interfered nodes.","PeriodicalId":360369,"journal":{"name":"2018 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129562258","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MIAC: Mutual-Information Classifier with ADASYN for Imbalanced Classification","authors":"Yanyu Cao, Xiaodong Zhao, Zhiping Zhou, Yufei Chen, Xianhui Liu, Yongming Lang","doi":"10.1109/SPAC46244.2018.8965597","DOIUrl":"https://doi.org/10.1109/SPAC46244.2018.8965597","url":null,"abstract":"currently, classification of imbalanced data is a significant issue in the area of data mining and machine learning because of the imbalance of most of the data set. An effective solution of this problem is Cost-Sensitive Learning (CSL), but when the costs are not given, this method cannot work property. As a Cost-Free Learning (CFL) method, Mutual-Information Classification (MIC) can obtain the optimal classification results when the cost information is not given. But this method emphasizes the data of minority class too much and neglects the accuracy of the classification of majority class. And based on the above, this paper presented a CFL method called Mutual-Information-ADASYN Classification (MIAC). Firstly, we get the abstaining samples which are hard to be classified by using MIC. Then we use these abstention samples to synthesize new instance by using the method of ADASYN. Thirdly, we build Mutual- Information-ADASYN Classification using the new samples. Finally, we use our classifier to get the final results. We evaluated the performance of MIAC on several imbalance binary datasets with different imbalance ratios. The experimental results indicate that the MIAC is more effective than MIC on dealing with imbalanced datasets.","PeriodicalId":360369,"journal":{"name":"2018 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122449195","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fuzzy Neural Networks (FNNs) Training Algorithm With Dropout via Its Equivalent Fully Connected Fuzzy Inference Systems (F-CONFIS)","authors":"Jing Wang, Philip Chen, Zhenyuan Ma, Zhenghong Xiao","doi":"10.1109/SPAC46244.2018.8965447","DOIUrl":"https://doi.org/10.1109/SPAC46244.2018.8965447","url":null,"abstract":"Fuzzy neural network (FNN) often suffers from overfitting problem, especially when FNN has large number of parameters. In the FNN system, there are two types of adjustable parameters, one is control parameters, and the other is link weights of consequent part. To improve convergent rate, Dropout technique is first adopted for Fuzzy neural network. A new training algorithm with dropout technique for FNN is proposed via its equivalent F-CONFIS. Illustrative examples are provided for checking the validity of the proposed method. Simulation attained satisfactory results. Proposed method for Fuzzy neural network via F-CONFIS has its rising values in all practical applications, such as system identification, expert System and image information processing system …, etc.","PeriodicalId":360369,"journal":{"name":"2018 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)","volume":"90 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124193681","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hardware Architecture for Real-Time Depth-image-based Rendering System","authors":"Ran Liu, Miao Xu, Yanzhen Zhang, Yangting Zheng, Yang Zhao, Yaqiong Liu","doi":"10.1109/SPAC46244.2018.8965589","DOIUrl":"https://doi.org/10.1109/SPAC46244.2018.8965589","url":null,"abstract":"This paper presents a hardware architecture for real-time depth-image-based rendering (DIBR) system. The shift-sensor camera setup is used in this system so that hardware-efficient algorithms can be applied to reduce the computational complexity and hardware cost. For the computational complexity, we propose a 3D image warping algorithm that requires no camera parameters to replace the complicated homographic transform, and a hole-filling method based on the disparity map to replace the complicated inpainting algorithm. For the hardware cost, we propose a fold-elimination approach based on view-judgment to replace the Z-buffer algorithm, and the row-level pipelining to reduce the requirement of external memory for disparity maps and reference images. Experimental results show that our implementation can achieve the similar synthesis quality as the quality implemented by software.","PeriodicalId":360369,"journal":{"name":"2018 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126415549","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Construction and Application of Knowledge Graph System in Computer Science","authors":"Tianjie Wang, Yihui Wang, Chengxiang Tan","doi":"10.1109/SPAC46244.2018.8965547","DOIUrl":"https://doi.org/10.1109/SPAC46244.2018.8965547","url":null,"abstract":"This paper proposes a method for constructing a knowledge graph system for computer science. According to the domain knowledge collected and summarized, the data mapping model is established by analyzing the structure of the data, and the data is parsed by the stream processing method. Then, the property graph model is established according to the structure of the data to obtain the entity, the attributes of the entity and the relationship between the entities. The Neo4j graph database is also used as a storage carrier to realize the construction of the knowledge graph system in the field of computer science.","PeriodicalId":360369,"journal":{"name":"2018 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)","volume":"2010 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127338932","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Coarse-to-fine Double Linear Iteration Method for Generating Superpixels","authors":"Jianan Shen, Zhiping Zhou, Xianhui Liu","doi":"10.1109/SPAC46244.2018.8965470","DOIUrl":"https://doi.org/10.1109/SPAC46244.2018.8965470","url":null,"abstract":"To improve the performance of the superpixel segmentation algorithm and extract the edge information of the input image, a coarse-to-fine double linear iteration (CDLI) method for generating superpixels is proposed in this article. CDLI can be seen as an improvement of the SLIC (simple linear iterative cluster). In order to improve the accuracy of the superpixel segmentation algorithm, the density of the input image should be uneven. Regions with sparce edge information should have lower superpixel density while regions with rich edge information should have higher superpixel density. In order to achieve this goal, CDLI coarsely partitions the input image firstly, with a method which is similar to the SLIC. On the basis of rough segmentation, the region that needs further fine segmentation is selected. Finally, the selected region is partitioned with higher precision to form the final result. The entire process is equivalent to running SLIC twice. In a series of experiments, it shows that CDLI significantly improves the segmentation accuracy of the SLIC by nearly 1 percentage point while slightly reducing the time efficiency of the SLIC.","PeriodicalId":360369,"journal":{"name":"2018 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)","volume":"110 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131646806","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Color Image Watermarking Scheme Against Geometric Rotation Attacks Based on HVS and DCT-DWT","authors":"Yi-Lin Bei, Sai Qiao, Ming-Xia Liu, Xiaorong Zhu, Qian Zhang","doi":"10.1109/SPAC46244.2018.8965467","DOIUrl":"https://doi.org/10.1109/SPAC46244.2018.8965467","url":null,"abstract":"In view of poor robustness and weak resistance to geometric attack of traditional image watermarking algorithm, this paper proposes a color blind watermarking algorithm based on the combination of wavelet intermediate frequency (DWT) and discrete cosine transform (DCT). First, the original image is transformed from the RGB color spaces into the YCbCr color spaces in which the watermark is embedded into all the three color spaces. Then, the position in which watermark was embedded was chosen by Human Visual System (HVS), and the watermarking embedding is achieved by the proposed algorithm based on DCT and DWT transform. In this process, Zernike moments are used for rotation correction. The experimental results show that the algorithm is robust, and the invisible watermarking’s ability of anti–geometric –attacking has been improved.","PeriodicalId":360369,"journal":{"name":"2018 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133046271","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Camera Abnormal Movement and Foreign Object Invasion Detection Based on Cumulative Edge Distribution Probability Model","authors":"Xiangru Yu, Fudong Cai, Yimin Dou, Jinping Li","doi":"10.1109/SPAC46244.2018.8965465","DOIUrl":"https://doi.org/10.1109/SPAC46244.2018.8965465","url":null,"abstract":"In practice, there often exist some occasions where video surveillance can only be realized by using rechargeable batteries due to the high cost of power supply. In order to extend the battery life, images can only be taken at a certain interval. Obviously, image difference is an important method to obtain various changes in this case. Among these changes, foreign invasion is one of the focuses. However, due to bolt shedding, abnormal weather and other factors, the camera may have abnormal movement which may seriously affect the detection effect of image difference method. Therefore, it is an urgent issue to find an effective way to detect the abnormal movement of camera and improve the detection accuracy of foreign invasion. Considering the characteristics of this kind of image sequences, we propose an effective algorithm to detect the camera abnormal movement and foreign object invasion based on a cumulative edge distribution probability model. Since sky region is relatively simple, we only discuss changes in sky region to detect camera abnormal movement. Our algorithm has 6 basic steps: firstly, segment the sky region; secondly, extract the edge information of the current image and the preceding adjacent image in the image sequence; thirdly, determine if the edge information of two adjacent images coincide. If consistent, then go to the next step, otherwise, it indicates that the camera has abnormal movement, then alarm; fourthly, calculate the cumulative edge probability distribution model in the sky region by using the historical image sequence; fifthly, by using adaptive Parzen window, determine if foreign object invasion exists by comparing the probability model of cumulative edge distribution with edge distribution of the current image; sixthly, update the cumulative edge distribution probability model in sky region. The algorithm achieves good results in practical applications. Through the test of thousands of images taken in the wild, the detection accuracy of camera abnormal movement and foreign object invasion reaches 95%.","PeriodicalId":360369,"journal":{"name":"2018 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129396347","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}