In order to transmit the encrypted video signal on the TI Company's digital signal processor DM642, we put forward a video encrypted algorithm based on the multiple logistic mapping. It combines H. 264 coding standard and use the chaotic sequences produced by three logistic mapping to scramble and transform the quantitative data. Moreover, it ensures that different frames have different encrypted sequences. The results of the practical operation on DM642 show that the algorithm has better initial value sensitivity and larger key space and can resist exhaustive attack, differential attack, and chosen-plaintext attack effectively. It can also meet the need of real-time very well with safety guarantee.
{"title":"A Multiple Chaotic Video Encryption System Based on DM642","authors":"Xiangzhu Long, Jishen Li, Youjun Hu","doi":"10.1109/CIS.2013.66","DOIUrl":"https://doi.org/10.1109/CIS.2013.66","url":null,"abstract":"In order to transmit the encrypted video signal on the TI Company's digital signal processor DM642, we put forward a video encrypted algorithm based on the multiple logistic mapping. It combines H. 264 coding standard and use the chaotic sequences produced by three logistic mapping to scramble and transform the quantitative data. Moreover, it ensures that different frames have different encrypted sequences. The results of the practical operation on DM642 show that the algorithm has better initial value sensitivity and larger key space and can resist exhaustive attack, differential attack, and chosen-plaintext attack effectively. It can also meet the need of real-time very well with safety guarantee.","PeriodicalId":294223,"journal":{"name":"2013 Ninth International Conference on Computational Intelligence and Security","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116712818","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}
Recently, technologies on reducing energy consumption of data centers have drawn considerable attentions. One constructive way is to improve energy efficiency of servers. Aiming at this goal, we propose a new energy-aware optimization model based on the combination of data placement and task scheduling in this paper. The main contributions are: (1)The impact of servers' performance on energy consumption is explored. (2) The model guarantees 100% data locality to save network bandwidth. (3) As tasks involved in cloud computing are usually tens of thousands, in order to solve this large scale optimization model efficiently, specific-design encoding and decoding methods are introduced. Based on these, an effective evolutionary algorithm is proposed. Finally, numerical experiments are made and the results indicate the effectiveness of the proposed algorithm.
{"title":"An Energy-Aware Optimization Model Based on Data Placement and Task Scheduling","authors":"Xiaoli Wang, Yuping Wang, Kun Meng","doi":"10.1109/CIS.2013.17","DOIUrl":"https://doi.org/10.1109/CIS.2013.17","url":null,"abstract":"Recently, technologies on reducing energy consumption of data centers have drawn considerable attentions. One constructive way is to improve energy efficiency of servers. Aiming at this goal, we propose a new energy-aware optimization model based on the combination of data placement and task scheduling in this paper. The main contributions are: (1)The impact of servers' performance on energy consumption is explored. (2) The model guarantees 100% data locality to save network bandwidth. (3) As tasks involved in cloud computing are usually tens of thousands, in order to solve this large scale optimization model efficiently, specific-design encoding and decoding methods are introduced. Based on these, an effective evolutionary algorithm is proposed. Finally, numerical experiments are made and the results indicate the effectiveness of the proposed algorithm.","PeriodicalId":294223,"journal":{"name":"2013 Ninth International Conference on Computational Intelligence and Security","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117238514","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}
This paper proposes a new architecture for the secure application of robust perceptual hashing. Protocols based on architecture of four parties are designed to ensure that no party can get the knowledge that may break the security of the hashing. A commutative hashing and encryption method that can generate multi-media hash on encryption domain is proposed to further resolve the privacy and security issues in the application of robust perceptual hashing.
{"title":"Secure Architecture and Protocols for Robust Perceptual Hashing","authors":"Donghui Hu, Bin Su, Shuli Zheng, Zhuang Zhang","doi":"10.1109/CIS.2013.122","DOIUrl":"https://doi.org/10.1109/CIS.2013.122","url":null,"abstract":"This paper proposes a new architecture for the secure application of robust perceptual hashing. Protocols based on architecture of four parties are designed to ensure that no party can get the knowledge that may break the security of the hashing. A commutative hashing and encryption method that can generate multi-media hash on encryption domain is proposed to further resolve the privacy and security issues in the application of robust perceptual hashing.","PeriodicalId":294223,"journal":{"name":"2013 Ninth International Conference on Computational Intelligence and Security","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126509134","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}
Traffic Flow Detection plays an important role in the field of Intelligent Transportation Systems (ITS). Traffic flow detection focuses on the detection and segmentation of video object. The most existing methods need to implement complex background modeling, and accordingly increase the computing complexity and computing cost. In order to reduce the computing cost of the vehicle detection, we propose a new vehicle detection method based on saliency energy image (SEI) and saliency motion energy image (SMEI) for automatic traffic flow detection. First, we set the detecting region of objects, and computing image saliency map of the detecting region for each frame. Then saliency energy image (SEI) and saliency motion energy image (SMEI) are calculated. Finally, the vehicle flow is detected by combining the vertical projection histogram of the SEIs and the binary SMEIs within pre-set virtual detecting box. Experimental results show that our method can work in real-time with a high accuracy and robustness to noise.
{"title":"A Novel Vehicle Flow Detection Algorithm Based on Motion Saliency for Traffic Surveillance System","authors":"Renlong Pan, Xin Lin, Chenquan Huang, Lin Wang","doi":"10.1109/CIS.2013.58","DOIUrl":"https://doi.org/10.1109/CIS.2013.58","url":null,"abstract":"Traffic Flow Detection plays an important role in the field of Intelligent Transportation Systems (ITS). Traffic flow detection focuses on the detection and segmentation of video object. The most existing methods need to implement complex background modeling, and accordingly increase the computing complexity and computing cost. In order to reduce the computing cost of the vehicle detection, we propose a new vehicle detection method based on saliency energy image (SEI) and saliency motion energy image (SMEI) for automatic traffic flow detection. First, we set the detecting region of objects, and computing image saliency map of the detecting region for each frame. Then saliency energy image (SEI) and saliency motion energy image (SMEI) are calculated. Finally, the vehicle flow is detected by combining the vertical projection histogram of the SEIs and the binary SMEIs within pre-set virtual detecting box. Experimental results show that our method can work in real-time with a high accuracy and robustness to noise.","PeriodicalId":294223,"journal":{"name":"2013 Ninth International Conference on Computational Intelligence and Security","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128264809","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}
This paper presents a new interactive way to solve the problem of human-computer interaction, with a pen as the media to achieve some operations. Firstly, this paper proposes a particle filter algorithm based on immune mechanism, and extracts the user pen operations by camera video. Then extract the line of pen's motor trend pen movement trend with the least square method and judge the legality of the pen operation and calculate the operation direction of the pen. Finally, we will gain mapping relation in the change in of machine interface.
{"title":"A Distance Interaction Method Based on Pen","authors":"Wang Lei, Gao Kuo, Qiaoyong Jiang, Zou Fen","doi":"10.1109/CIS.2013.147","DOIUrl":"https://doi.org/10.1109/CIS.2013.147","url":null,"abstract":"This paper presents a new interactive way to solve the problem of human-computer interaction, with a pen as the media to achieve some operations. Firstly, this paper proposes a particle filter algorithm based on immune mechanism, and extracts the user pen operations by camera video. Then extract the line of pen's motor trend pen movement trend with the least square method and judge the legality of the pen operation and calculate the operation direction of the pen. Finally, we will gain mapping relation in the change in of machine interface.","PeriodicalId":294223,"journal":{"name":"2013 Ninth International Conference on Computational Intelligence and Security","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121337954","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}
To ensure property safety, risk assessment plays an essential role in modern society. Credit scoring, which is a significant branch of exposure rating, becomes a hot topic. As a result, various kinds of credit scoring models are established to evaluate the customers' credit rank. In this paper, a simple credit scoring model, Collaborative Filtering based on Matrix Factorization with data whose continuous attributes are discretized considering Information Entropy (CF-MF-D-IE), is constructed to solve credit scoring issues. The proposed model is tested on two important credit data sets in UCI Repository of Machine Learning databases. Compared with Collaborative Filtering using non-discretized data and Support Vector Machines with discretized data, CF-MF-D-IE has better classification accuracy rate.
{"title":"A Credit Scoring Model Based on Collaborative Filtering","authors":"Xin Zheng","doi":"10.1109/CIS.2013.37","DOIUrl":"https://doi.org/10.1109/CIS.2013.37","url":null,"abstract":"To ensure property safety, risk assessment plays an essential role in modern society. Credit scoring, which is a significant branch of exposure rating, becomes a hot topic. As a result, various kinds of credit scoring models are established to evaluate the customers' credit rank. In this paper, a simple credit scoring model, Collaborative Filtering based on Matrix Factorization with data whose continuous attributes are discretized considering Information Entropy (CF-MF-D-IE), is constructed to solve credit scoring issues. The proposed model is tested on two important credit data sets in UCI Repository of Machine Learning databases. Compared with Collaborative Filtering using non-discretized data and Support Vector Machines with discretized data, CF-MF-D-IE has better classification accuracy rate.","PeriodicalId":294223,"journal":{"name":"2013 Ninth International Conference on Computational Intelligence and Security","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132197791","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}
Forensics analysis of physical memory is a key point in computer living forensics. Most of the research carried out focusing on enumerating processes and threads by accessing memory resident objects. However, collecting case sensitive information from the extracted memory content is import and difficult in computer forensics. Password plaintext is one of the most concerning sensitive information to an investigator. The traditional methods to extract system logged in password plaintext mainly rely on cracker tools, whose success rate depend on the password complexity. The important contribution of the paper is a new technique for extracting system logged-in password plaintext from physical memory. It allows extracting arbitrary length system logged-in password plaintext. The proposed method can extract system logged-in password plaintext of Windows XP and Windows 7.
{"title":"Research on Extracting System Logged-In Password Forensically from Windows Memory Image File","authors":"Lijuan Xu, Lianhai Wang","doi":"10.1109/CIS.2013.156","DOIUrl":"https://doi.org/10.1109/CIS.2013.156","url":null,"abstract":"Forensics analysis of physical memory is a key point in computer living forensics. Most of the research carried out focusing on enumerating processes and threads by accessing memory resident objects. However, collecting case sensitive information from the extracted memory content is import and difficult in computer forensics. Password plaintext is one of the most concerning sensitive information to an investigator. The traditional methods to extract system logged in password plaintext mainly rely on cracker tools, whose success rate depend on the password complexity. The important contribution of the paper is a new technique for extracting system logged-in password plaintext from physical memory. It allows extracting arbitrary length system logged-in password plaintext. The proposed method can extract system logged-in password plaintext of Windows XP and Windows 7.","PeriodicalId":294223,"journal":{"name":"2013 Ninth International Conference on Computational Intelligence and Security","volume":"107 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132715235","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}
The glowworm swarm optimization algorithm is used to solve multi-objective optimization problem (MOP-GSO). It is shown by simulation that, MOP-GSO algorithm is effective to solve multi-objective optimization. Compared with NSGA2, it is better in term of the spread of the solutions.
{"title":"Glowworm Swarm Optimization Algorithm for Solving Multi-objective Optimization Problem","authors":"He Deng-xu, Liu Gui-qing, Zhu Hua-zheng","doi":"10.1109/CIS.2013.10","DOIUrl":"https://doi.org/10.1109/CIS.2013.10","url":null,"abstract":"The glowworm swarm optimization algorithm is used to solve multi-objective optimization problem (MOP-GSO). It is shown by simulation that, MOP-GSO algorithm is effective to solve multi-objective optimization. Compared with NSGA2, it is better in term of the spread of the solutions.","PeriodicalId":294223,"journal":{"name":"2013 Ninth International Conference on Computational Intelligence and Security","volume":"82 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116347251","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}
Vehicular ad hoc network (VANET) is a type of promising application deployed along a road for communication, safety and emergency information delivery, data collection, and entertainment. The network efficiency and stability are of great concern. We devote to achieve the communication quality by appropriate gateways. The channel randomness is taken into account in the wireless network, because it can affect the stability of the communication link. We model the wireless channels to analyze the VANET efficiency, then propose an efficient gateway selection mechanism based on multiple metrics: correctly decoded probability, network delay and relative velocity. These metrics are calculated by the Simple Additive Weighting (SAW) technique to choose the optimum gateway nodes. Simulation results show that our scheme can improve the quality of the VANET-3G network by analysis and compare.
{"title":"A Multiple Metrics Gateway Selection Algorithm for Vehicular Ad Hoc Networks in Fading Channels","authors":"Kelei Chen, Lijia Chen, Junyong Mao, Dong Zhao","doi":"10.1109/CIS.2013.142","DOIUrl":"https://doi.org/10.1109/CIS.2013.142","url":null,"abstract":"Vehicular ad hoc network (VANET) is a type of promising application deployed along a road for communication, safety and emergency information delivery, data collection, and entertainment. The network efficiency and stability are of great concern. We devote to achieve the communication quality by appropriate gateways. The channel randomness is taken into account in the wireless network, because it can affect the stability of the communication link. We model the wireless channels to analyze the VANET efficiency, then propose an efficient gateway selection mechanism based on multiple metrics: correctly decoded probability, network delay and relative velocity. These metrics are calculated by the Simple Additive Weighting (SAW) technique to choose the optimum gateway nodes. Simulation results show that our scheme can improve the quality of the VANET-3G network by analysis and compare.","PeriodicalId":294223,"journal":{"name":"2013 Ninth International Conference on Computational Intelligence and Security","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114818232","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}
In the image inpainting process, the data term of the Criminisi algorithm depends on the shape of the manually selected target region and the confidence drops to zero rapidly, resulting in in painting sequence deviation which finally influence the in paint effect. Then we introduce the entropy to improve the data term, in this way another priority function will be defined. Experiments confirm that the improved algorithm can eliminate the dependence on the shape of the target region and the confidence will not drop to zero rapidly again. Experiments show that the algorithm will repair the image with pure texture, strong edges and purely synthetic images better.
{"title":"Improved Criminisi Algorithm Based on a New Priority Function with the Gray Entropy","authors":"Xiang-yan Xi, F. Wang, Yefei Liu","doi":"10.1109/CIS.2013.52","DOIUrl":"https://doi.org/10.1109/CIS.2013.52","url":null,"abstract":"In the image inpainting process, the data term of the Criminisi algorithm depends on the shape of the manually selected target region and the confidence drops to zero rapidly, resulting in in painting sequence deviation which finally influence the in paint effect. Then we introduce the entropy to improve the data term, in this way another priority function will be defined. Experiments confirm that the improved algorithm can eliminate the dependence on the shape of the target region and the confidence will not drop to zero rapidly again. Experiments show that the algorithm will repair the image with pure texture, strong edges and purely synthetic images better.","PeriodicalId":294223,"journal":{"name":"2013 Ninth International Conference on Computational Intelligence and Security","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134428181","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}
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