Pub Date : 2017-09-01DOI: 10.1109/ICRITO.2017.8342466
Kai-duan Cao, Jing He, Wenqing Fan, Wei Huang, Lei Chen, Yue Pan
To detect the vulnerabilities of Web applications which based on the PHP scripting language. This paper proposes a PHP vulnerability detection method based on fine-grained taint analysis algorithm. First of all, this article generates the Abstract Syntax Tree by lexical and grammatical analysis on the PHP, and then produces the corresponding Control Flow Graph. At last, performing taint analysis on the Control Flow Graph. By tracking the program parameters, variables and other external input, marking the input type, propagating to various types of vulnerability function via the taint, and finally according to the tainted types of variable which are outputted to identify the vulnerabilities. We tested 16 programs of Damm Vulnerable Web App and found nine known vulnerabilities.
{"title":"PHP vulnerability detection based on taint analysis","authors":"Kai-duan Cao, Jing He, Wenqing Fan, Wei Huang, Lei Chen, Yue Pan","doi":"10.1109/ICRITO.2017.8342466","DOIUrl":"https://doi.org/10.1109/ICRITO.2017.8342466","url":null,"abstract":"To detect the vulnerabilities of Web applications which based on the PHP scripting language. This paper proposes a PHP vulnerability detection method based on fine-grained taint analysis algorithm. First of all, this article generates the Abstract Syntax Tree by lexical and grammatical analysis on the PHP, and then produces the corresponding Control Flow Graph. At last, performing taint analysis on the Control Flow Graph. By tracking the program parameters, variables and other external input, marking the input type, propagating to various types of vulnerability function via the taint, and finally according to the tainted types of variable which are outputted to identify the vulnerabilities. We tested 16 programs of Damm Vulnerable Web App and found nine known vulnerabilities.","PeriodicalId":357118,"journal":{"name":"2017 6th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO)","volume":"63 12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126129190","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}
Pub Date : 2017-09-01DOI: 10.1109/ICRITO.2017.8342494
Nafhath Rasheeda Rafiq, Sadia Fatima Mohammed, Jitendra Pandey, A. Singh
Internet of Things has gained a lot of hype in a very short period and is being adopted by varied sectors in order to attain its potential by bringing advanced level of automation in the respective industry based activities to reap the benefits. The commercial real estate is one such domain that many countries are focusing on transforming their buildings and infrastructure to have the cities contributing towards to become as ‘smarter cities’. The intelligent buildings are where the schools, offices, residential complexes integrate IoT for enhancing the capabilities and efficiency of the building infrastructure. The fundamental drivers that have encouraged for smart buildings in the market place are meeting the goals of corporate social responsibility, revenue growth and improving operational efficiency. With the demand for deployment of smart buildings on the rise, there have been many key trends that have emerged making it a thrust to have revolutionized transformation for digitalizing the commercial buildings, homes, etc. by means of effective smart solutions. The paper critically analyses the role of IoT in smart buildings, the latest trends that are demanding for a shift to smart buildings, and highlights the challenges for smart buildings implementation.
{"title":"Classic from the outside, smart from the inside: The era of smart buildings","authors":"Nafhath Rasheeda Rafiq, Sadia Fatima Mohammed, Jitendra Pandey, A. Singh","doi":"10.1109/ICRITO.2017.8342494","DOIUrl":"https://doi.org/10.1109/ICRITO.2017.8342494","url":null,"abstract":"Internet of Things has gained a lot of hype in a very short period and is being adopted by varied sectors in order to attain its potential by bringing advanced level of automation in the respective industry based activities to reap the benefits. The commercial real estate is one such domain that many countries are focusing on transforming their buildings and infrastructure to have the cities contributing towards to become as ‘smarter cities’. The intelligent buildings are where the schools, offices, residential complexes integrate IoT for enhancing the capabilities and efficiency of the building infrastructure. The fundamental drivers that have encouraged for smart buildings in the market place are meeting the goals of corporate social responsibility, revenue growth and improving operational efficiency. With the demand for deployment of smart buildings on the rise, there have been many key trends that have emerged making it a thrust to have revolutionized transformation for digitalizing the commercial buildings, homes, etc. by means of effective smart solutions. The paper critically analyses the role of IoT in smart buildings, the latest trends that are demanding for a shift to smart buildings, and highlights the challenges for smart buildings implementation.","PeriodicalId":357118,"journal":{"name":"2017 6th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126343592","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}
Pub Date : 2017-09-01DOI: 10.1109/ICRITO.2017.8342508
Sudhriti Sengupta, Neetu Mittal
Skin lesions are any abnormal pattern in the skin of a patient occurring due to some disease, accident etc. Skin lesions images are very important for the doctors to diagnosis disease and treat the patients. Image processing techniques are applied on these images to improve the quality of the image in terms of visibility of the different feature in the image. Enhanced images aid the doctors and healthcare provider to provide better treatment to the patients. Feature extraction of skin lesion images is very significant for classification of skin lesion which helps in identification of skin lesions. Many techniques have been proposed for feature extraction of images. This paper provides a brief overview of the feature extraction techniques used in skin lesion images and observes some areas in which further work can be done.
{"title":"Analysis of various techniques of feature extraction on skin lesion images","authors":"Sudhriti Sengupta, Neetu Mittal","doi":"10.1109/ICRITO.2017.8342508","DOIUrl":"https://doi.org/10.1109/ICRITO.2017.8342508","url":null,"abstract":"Skin lesions are any abnormal pattern in the skin of a patient occurring due to some disease, accident etc. Skin lesions images are very important for the doctors to diagnosis disease and treat the patients. Image processing techniques are applied on these images to improve the quality of the image in terms of visibility of the different feature in the image. Enhanced images aid the doctors and healthcare provider to provide better treatment to the patients. Feature extraction of skin lesion images is very significant for classification of skin lesion which helps in identification of skin lesions. Many techniques have been proposed for feature extraction of images. This paper provides a brief overview of the feature extraction techniques used in skin lesion images and observes some areas in which further work can be done.","PeriodicalId":357118,"journal":{"name":"2017 6th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114184379","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}
Pub Date : 2017-09-01DOI: 10.1109/ICRITO.2017.8342419
K. Gupta, K. Kishore, S. C. Jain
Use of sensing technique is now a synonym to industrial work environment and detecting presence of hazardous gases in various industrial processes is nowadays mandatory. This aforementioned fact is realized in this paper, wherein, an integrated IoT server is build using nodejs, utilizing MQTT for M2M communication. The framework is designed for auto connection of sensor nodes as soon as they are powered up and the central server is used for analyzing the incoming sensor data of (CEERIs) ammonia gas sensor. The data collection is made over a period of 4 months with identical gas exposure in two exposure cycle. Here, Raspberry pi3 board is used as a gateway that implements the IoT server and hotspot for device connectivity. Sensors study includes baseline and sensitivity drift, the relative change in response to gas exposure and hysteresis effect together. A correlation of above-mentioned sensor parameters depicts mutual relation and effect of sensor parameters on each other. The industrial safety is also guaranteed by routine sensor health monitoring and possible replacement of the field sensor. The degradation study presented in this paper suggests the possible lifetime of the sensor by utilizing AR and NLRAX prediction models and when it is viable to replace the field sensor.
{"title":"Quality assessment and drift analysis of IoT enabled ammonia sensor","authors":"K. Gupta, K. Kishore, S. C. Jain","doi":"10.1109/ICRITO.2017.8342419","DOIUrl":"https://doi.org/10.1109/ICRITO.2017.8342419","url":null,"abstract":"Use of sensing technique is now a synonym to industrial work environment and detecting presence of hazardous gases in various industrial processes is nowadays mandatory. This aforementioned fact is realized in this paper, wherein, an integrated IoT server is build using nodejs, utilizing MQTT for M2M communication. The framework is designed for auto connection of sensor nodes as soon as they are powered up and the central server is used for analyzing the incoming sensor data of (CEERIs) ammonia gas sensor. The data collection is made over a period of 4 months with identical gas exposure in two exposure cycle. Here, Raspberry pi3 board is used as a gateway that implements the IoT server and hotspot for device connectivity. Sensors study includes baseline and sensitivity drift, the relative change in response to gas exposure and hysteresis effect together. A correlation of above-mentioned sensor parameters depicts mutual relation and effect of sensor parameters on each other. The industrial safety is also guaranteed by routine sensor health monitoring and possible replacement of the field sensor. The degradation study presented in this paper suggests the possible lifetime of the sensor by utilizing AR and NLRAX prediction models and when it is viable to replace the field sensor.","PeriodicalId":357118,"journal":{"name":"2017 6th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130263627","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}
Pub Date : 2017-09-01DOI: 10.1109/ICRITO.2017.8342425
Prabhpahul Singh, R. Malhotra
Software defect prediction is a well renowned field of software engineering. Determination of defective classes early in the lifecycle of a software product helps software practitioners in effective allocation of resources. More resources are allocated to probable defective classes so that defects can be removed in the initial phases of the software product. Such a practice would lead to a good quality software product. Although, hundreds of defect prediction models have been developed and validated by researchers, there is still a need to develop and evaluate more models to draw generalized conclusions. Literature studies have found Machine Learning (ML) algorithms to be effective classifiers in this domain. Thus, this study evaluates four ML algorithms on data collected from seven open source software projects for developing software defect prediction models. The results indicate superior performance of the Multilayer Perceptron algorithm over all the other investigated algorithms. The results of the study are also statistically evaluated to establish their effectiveness.
{"title":"Assessment of machine learning algorithms for determining defective classes in an object-oriented software","authors":"Prabhpahul Singh, R. Malhotra","doi":"10.1109/ICRITO.2017.8342425","DOIUrl":"https://doi.org/10.1109/ICRITO.2017.8342425","url":null,"abstract":"Software defect prediction is a well renowned field of software engineering. Determination of defective classes early in the lifecycle of a software product helps software practitioners in effective allocation of resources. More resources are allocated to probable defective classes so that defects can be removed in the initial phases of the software product. Such a practice would lead to a good quality software product. Although, hundreds of defect prediction models have been developed and validated by researchers, there is still a need to develop and evaluate more models to draw generalized conclusions. Literature studies have found Machine Learning (ML) algorithms to be effective classifiers in this domain. Thus, this study evaluates four ML algorithms on data collected from seven open source software projects for developing software defect prediction models. The results indicate superior performance of the Multilayer Perceptron algorithm over all the other investigated algorithms. The results of the study are also statistically evaluated to establish their effectiveness.","PeriodicalId":357118,"journal":{"name":"2017 6th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131235322","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}
Pub Date : 2017-09-01DOI: 10.1109/ICRITO.2017.8342488
Divya Singh, Sunita Prasad, Sandeep Srivastava
This paper presents the hardware development and implementation of artificial intelligence based neuron cell using swarm intelligence based algorithm (AVT-PSO) where the functionality of the architecture is tested by implementing four bit addition based on cognitive science neural network employing five neuron cell (sort of a model emulating function of a neuron cell in the brain) trained using an adaptive velocity threshold particle swarm optimization on Spartan-3e XC3S100E Field Programmable Gate Arrays (FPGA). Each neuron cell represents a processing element which is trained using swarm intelligence. Adaptive velocity threshold PSO algorithm is used in evolving threshold values to train the weights of neural cells. Implemented system is flexible in design, allowing the possibility to add or remove neurons to generate new network architectures.
本文介绍了使用基于群智能算法(AVT-PSO)的基于人工智能的神经元细胞的硬件开发和实现，其中通过在spartan3e XC3S100E Field Programmable上使用自适应速度阈值粒子群优化训练的五个神经元细胞(一种模拟大脑神经元细胞功能的模型)实现基于认知科学神经网络的四位加法来测试该架构的功能门阵列(FPGA)。每个神经元细胞代表一个处理单元，该处理单元使用群体智能进行训练。采用自适应速度阈值粒子群算法对阈值进行演化，训练神经细胞的权值。实现的系统设计灵活，允许添加或删除神经元以生成新的网络架构。
{"title":"Implementation of artificial intelligence cognitive neuroscience neuron cell using adaptive velocity threshold particle swarm optimization (AVT-PSO) on FPGA","authors":"Divya Singh, Sunita Prasad, Sandeep Srivastava","doi":"10.1109/ICRITO.2017.8342488","DOIUrl":"https://doi.org/10.1109/ICRITO.2017.8342488","url":null,"abstract":"This paper presents the hardware development and implementation of artificial intelligence based neuron cell using swarm intelligence based algorithm (AVT-PSO) where the functionality of the architecture is tested by implementing four bit addition based on cognitive science neural network employing five neuron cell (sort of a model emulating function of a neuron cell in the brain) trained using an adaptive velocity threshold particle swarm optimization on Spartan-3e XC3S100E Field Programmable Gate Arrays (FPGA). Each neuron cell represents a processing element which is trained using swarm intelligence. Adaptive velocity threshold PSO algorithm is used in evolving threshold values to train the weights of neural cells. Implemented system is flexible in design, allowing the possibility to add or remove neurons to generate new network architectures.","PeriodicalId":357118,"journal":{"name":"2017 6th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO)","volume":"92 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123762478","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}
Pub Date : 2017-09-01DOI: 10.1109/ICRITO.2017.8342442
R. Malhotra, Anshul Khurana
Defect prediction of software is necessary to determine defective parts of software. Defect prediction models are elaborated with the help of software metrics when combined with defective data to predict the classes that are defective. In this paper we have used datasets that statistically resolve the relationship among software metrics and defect vulnerability. The main intent of this paper are 1) Feature selection for defect prediction using proposed evolutionary algorithm 2) Comparing machine learning techniques 3) Use of precision and recall as performance measure for defect prediction 4) 10- fold validation is performed on every model. In this discourse, we predict defective class using 5 machine learning techniques and 2 evolutionary techniques for feature selection. In this work, we have applied evolutionary algorithms for feature selection suitable for each of the classification techniques applied on five open source android packages. Finally, for validation of calculated results, 10-fold validation is used. The results show that using evolutionary algorithms for feature selection can improve precision and recall for RF, DT and SVM. Precision and recall have best rise using SVM model. The use of evolutionary algorithms don't effect precision and recall for statistical classifier. The results obtained from evaluation thus confirm about the prediction of default classes using evolutionary algorithms is better than using only machine learning techniques. The analyzed and calculated results gave us the view about the usage of evolutionary algorithm with statistical classifier is of no use.
{"title":"Analysis of evolutionary algorithms to improve software defect prediction","authors":"R. Malhotra, Anshul Khurana","doi":"10.1109/ICRITO.2017.8342442","DOIUrl":"https://doi.org/10.1109/ICRITO.2017.8342442","url":null,"abstract":"Defect prediction of software is necessary to determine defective parts of software. Defect prediction models are elaborated with the help of software metrics when combined with defective data to predict the classes that are defective. In this paper we have used datasets that statistically resolve the relationship among software metrics and defect vulnerability. The main intent of this paper are 1) Feature selection for defect prediction using proposed evolutionary algorithm 2) Comparing machine learning techniques 3) Use of precision and recall as performance measure for defect prediction 4) 10- fold validation is performed on every model. In this discourse, we predict defective class using 5 machine learning techniques and 2 evolutionary techniques for feature selection. In this work, we have applied evolutionary algorithms for feature selection suitable for each of the classification techniques applied on five open source android packages. Finally, for validation of calculated results, 10-fold validation is used. The results show that using evolutionary algorithms for feature selection can improve precision and recall for RF, DT and SVM. Precision and recall have best rise using SVM model. The use of evolutionary algorithms don't effect precision and recall for statistical classifier. The results obtained from evaluation thus confirm about the prediction of default classes using evolutionary algorithms is better than using only machine learning techniques. The analyzed and calculated results gave us the view about the usage of evolutionary algorithm with statistical classifier is of no use.","PeriodicalId":357118,"journal":{"name":"2017 6th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127840055","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}
Pub Date : 2017-09-01DOI: 10.1109/ICRITO.2017.8342453
P. Shrivas, U. Lilhore, Nitin Agrawal
As the quantity of web clients are expanding every day. This work concentrate on the retrieval of pictures by using the visual and annotation characteristics of the images. In this work two kind of features are utilized for the bunching of the picture dataset. So Based on the comparability of content and CCM components of the picture bunches are made. For bunching here genetic approach is utilized. Two phase learning genetic algorithm named as teacher learning based optimization was utilized for clustring. Here client pass two kind of queries first was content while other is image, this assistance in choosing suitable cluster for retrieval of picture. Analysis was done on genuine and artificial set of pictures. Result demonstrates that proposed work is better on various assessment parameters as contrast with existing strategies.
{"title":"Genetic approach based image retrieval by using CCM and textual features","authors":"P. Shrivas, U. Lilhore, Nitin Agrawal","doi":"10.1109/ICRITO.2017.8342453","DOIUrl":"https://doi.org/10.1109/ICRITO.2017.8342453","url":null,"abstract":"As the quantity of web clients are expanding every day. This work concentrate on the retrieval of pictures by using the visual and annotation characteristics of the images. In this work two kind of features are utilized for the bunching of the picture dataset. So Based on the comparability of content and CCM components of the picture bunches are made. For bunching here genetic approach is utilized. Two phase learning genetic algorithm named as teacher learning based optimization was utilized for clustring. Here client pass two kind of queries first was content while other is image, this assistance in choosing suitable cluster for retrieval of picture. Analysis was done on genuine and artificial set of pictures. Result demonstrates that proposed work is better on various assessment parameters as contrast with existing strategies.","PeriodicalId":357118,"journal":{"name":"2017 6th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO)","volume":"8 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126041088","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}
Pub Date : 2017-09-01DOI: 10.1109/ICRITO.2017.8342472
Naina Arya, Sonam Devgan Kaul
A wireless sensor network is a gathering of particular transducers with an interchange framework for checking and recording conditions at differing areas. As there is limited data storage and computational power in sensor nodes henceforth special outlines are required for cryptographic schemes in WSN's. In the existing protocols of group key transfer, all the nodes m jointly generate their secret, but a group of Λ (k < m) nodes can't generate its group key. In this paper, we have given this characteristic to all the nodes so that m or less nodes can generate their group key only after giving successful key generation request to the server. We have implemented the code based threshold scheme in the proposed group key transfer mechanism for wireless sensor environment and our proposed group key protocol is based upon linear secret sharing scheme, Vandermonde matrix, factoring problem and one-way hash function. The proposed scheme allows the implementation of threshold operations for the both group key generation and secret distribution and can also oppose potential assaults and furthermore essentially diminish the computational cost of the framework.
{"title":"Code based threshold scheme on group key transfer protocol for WSN's","authors":"Naina Arya, Sonam Devgan Kaul","doi":"10.1109/ICRITO.2017.8342472","DOIUrl":"https://doi.org/10.1109/ICRITO.2017.8342472","url":null,"abstract":"A wireless sensor network is a gathering of particular transducers with an interchange framework for checking and recording conditions at differing areas. As there is limited data storage and computational power in sensor nodes henceforth special outlines are required for cryptographic schemes in WSN's. In the existing protocols of group key transfer, all the nodes m jointly generate their secret, but a group of Λ (k < m) nodes can't generate its group key. In this paper, we have given this characteristic to all the nodes so that m or less nodes can generate their group key only after giving successful key generation request to the server. We have implemented the code based threshold scheme in the proposed group key transfer mechanism for wireless sensor environment and our proposed group key protocol is based upon linear secret sharing scheme, Vandermonde matrix, factoring problem and one-way hash function. The proposed scheme allows the implementation of threshold operations for the both group key generation and secret distribution and can also oppose potential assaults and furthermore essentially diminish the computational cost of the framework.","PeriodicalId":357118,"journal":{"name":"2017 6th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115300096","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}
Pub Date : 2017-09-01DOI: 10.1109/ICRITO.2017.8342484
Mohammed Abdullah Al Rasbi, A. Singh
Radio and Television have become the first platform for people to get information and plays a very important role in development of country and building of a society. On the other hand, an increase in the number of audience and media contents are creating many challenges related to broadcast and live streaming services such as Broadcasting Bandwidth, Media Storage, Quality of Services (QoS) and Resources Components like (Memory Usage, CPU load and Network traffic). Therefore, we are looking for platform for delivering scalable streaming services and provide better utilization with cut down both capital and operating costs. This paper talks about the needs and scope of Private Cloud Technology for Public Authority of Radio (PART) benefits and advantage of cloud computing adoption. The aim of this paper is to deliver private cloud technology platform based on streaming services. The proposed solution can be better for utilization of resources, increase Quality of Service and deliver improvement in overall performance, scalability and capacity with minimum cost.
广播电视已经成为人们获取信息的第一平台，在国家发展和社会建设中发挥着非常重要的作用。另一方面，观众数量和媒体内容的增加给广播和直播服务带来了许多挑战，如广播带宽、媒体存储、服务质量(QoS)和资源组件(内存使用、CPU负载和网络流量)。因此，我们正在寻找提供可扩展流媒体服务的平台，并在降低资本和运营成本的同时提供更好的利用率。本文讨论了公共无线电管理局(Public Authority of Radio, PART)采用私有云技术的需求和范围，以及采用云计算的好处和优势。本文旨在提供基于流媒体服务的私有云技术平台。建议的解决方案可以更好地利用资源，提高服务质量，并以最小的成本提供总体性能、可伸缩性和容量方面的改进。
{"title":"Need and scope of Private Cloud Technology for public authority for radio & television in Oman","authors":"Mohammed Abdullah Al Rasbi, A. Singh","doi":"10.1109/ICRITO.2017.8342484","DOIUrl":"https://doi.org/10.1109/ICRITO.2017.8342484","url":null,"abstract":"Radio and Television have become the first platform for people to get information and plays a very important role in development of country and building of a society. On the other hand, an increase in the number of audience and media contents are creating many challenges related to broadcast and live streaming services such as Broadcasting Bandwidth, Media Storage, Quality of Services (QoS) and Resources Components like (Memory Usage, CPU load and Network traffic). Therefore, we are looking for platform for delivering scalable streaming services and provide better utilization with cut down both capital and operating costs. This paper talks about the needs and scope of Private Cloud Technology for Public Authority of Radio (PART) benefits and advantage of cloud computing adoption. The aim of this paper is to deliver private cloud technology platform based on streaming services. The proposed solution can be better for utilization of resources, increase Quality of Service and deliver improvement in overall performance, scalability and capacity with minimum cost.","PeriodicalId":357118,"journal":{"name":"2017 6th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128448495","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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