Pub Date : 2021-01-01DOI: 10.1109/ICCCNT51525.2021.9579549
P. Suresh, M. Madhavu
{"title":"Insider Attack: Internal Cyber Attack Detection Using Machine Learning","authors":"P. Suresh, M. Madhavu","doi":"10.1109/ICCCNT51525.2021.9579549","DOIUrl":"https://doi.org/10.1109/ICCCNT51525.2021.9579549","url":null,"abstract":"","PeriodicalId":6581,"journal":{"name":"2017 8th International Conference on Computing, Communication and Networking Technologies (ICCCNT)","volume":"24 1","pages":"1-7"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83232414","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 : 2021-01-01DOI: 10.1109/ICCCNT51525.2021.9580105
Y. R. S. kumar, H. Champa
{"title":"A Fuzzy based Scheduling approach for Efficient Sensing in IoT-Cloud","authors":"Y. R. S. kumar, H. Champa","doi":"10.1109/ICCCNT51525.2021.9580105","DOIUrl":"https://doi.org/10.1109/ICCCNT51525.2021.9580105","url":null,"abstract":"","PeriodicalId":6581,"journal":{"name":"2017 8th International Conference on Computing, Communication and Networking Technologies (ICCCNT)","volume":"452 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77000675","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 : 2020-07-01DOI: 10.1109/ICCCNT49239.2020.9225316
N. Sumith
Mathematical model have long been used to understand several real world processes. They provide sufficient clarification and understanding. One such mathematical model in context of viral spread was epidemic model. Since then, this model has been used in various context including the spread of viral diseases in the population, information diffusion in social networks and so on. Recently, to fill in the gap seen in $SIR$ , a closed model, $R_{n}SIR$ was developed. An extension to $R_{n}SIR$ model, an open RnSIR model which includes the join and exit rates of users, is proposed in this paper. Through simulation on various social networks, the suitability of the model in mapping the information diffusion process in context of joining and exit rate of users is shown. This article discusses the dynamism of information spread and proposes a model can be used to understand spread of computer virus, the spread of epidemics.
{"title":"Open RnSIR model for Information Spread in Social Networks","authors":"N. Sumith","doi":"10.1109/ICCCNT49239.2020.9225316","DOIUrl":"https://doi.org/10.1109/ICCCNT49239.2020.9225316","url":null,"abstract":"Mathematical model have long been used to understand several real world processes. They provide sufficient clarification and understanding. One such mathematical model in context of viral spread was epidemic model. Since then, this model has been used in various context including the spread of viral diseases in the population, information diffusion in social networks and so on. Recently, to fill in the gap seen in $SIR$ , a closed model, $R_{n}SIR$ was developed. An extension to $R_{n}SIR$ model, an open RnSIR model which includes the join and exit rates of users, is proposed in this paper. Through simulation on various social networks, the suitability of the model in mapping the information diffusion process in context of joining and exit rate of users is shown. This article discusses the dynamism of information spread and proposes a model can be used to understand spread of computer virus, the spread of epidemics.","PeriodicalId":6581,"journal":{"name":"2017 8th International Conference on Computing, Communication and Networking Technologies (ICCCNT)","volume":"20 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81619116","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 : 2020-07-01DOI: 10.1109/ICCCNT49239.2020.9225547
R. Madhumathi, T. Arumuganathan, R. Shruthi
Agriculture plays a vital role in the economic development of our country. Crop yield primarily depends on soil fertility and moisture level. Fertilizers are normally recommended based on the nutrient present in the soil. To recommend a suitable fertilizer level, the soil nutrient analysis is essential which is done mostly using laboratory techniques. Manual methods of measuring soil nutrients are time consuming. Many farmers refrain to perform soil testing in the laboratory and grow the same crop in the land continuously, hence soil loses its fertility. A system has been proposed to adopt precision agriculture using Wireless Sensor Networks, which enables remote monitoring of soil fertility and other parameters namely soil moisture, pH and temperature. This data is transmitted to the cloud and the corresponding values are displayed on a mobile application. The proposed Internet of things (IoT) based software system has the intelligence to recommend the quantity of water and fertilizer which improves the quality of the soil and ensures optimum growth of the crop.
{"title":"Soil NPK and Moisture analysis using Wireless Sensor Networks","authors":"R. Madhumathi, T. Arumuganathan, R. Shruthi","doi":"10.1109/ICCCNT49239.2020.9225547","DOIUrl":"https://doi.org/10.1109/ICCCNT49239.2020.9225547","url":null,"abstract":"Agriculture plays a vital role in the economic development of our country. Crop yield primarily depends on soil fertility and moisture level. Fertilizers are normally recommended based on the nutrient present in the soil. To recommend a suitable fertilizer level, the soil nutrient analysis is essential which is done mostly using laboratory techniques. Manual methods of measuring soil nutrients are time consuming. Many farmers refrain to perform soil testing in the laboratory and grow the same crop in the land continuously, hence soil loses its fertility. A system has been proposed to adopt precision agriculture using Wireless Sensor Networks, which enables remote monitoring of soil fertility and other parameters namely soil moisture, pH and temperature. This data is transmitted to the cloud and the corresponding values are displayed on a mobile application. The proposed Internet of things (IoT) based software system has the intelligence to recommend the quantity of water and fertilizer which improves the quality of the soil and ensures optimum growth of the crop.","PeriodicalId":6581,"journal":{"name":"2017 8th International Conference on Computing, Communication and Networking Technologies (ICCCNT)","volume":"31 4 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82950594","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 : 2020-07-01DOI: 10.1109/ICCCNT49239.2020.9225314
G. Akilandeswary, J. Manickam
Recoding is the unique feature of Random Linear Network Coding (RLNC). It helps to reduce the number of transmissions. It provides low delay. RLNC is a next-generation network coding technique. Its ability to recover packet at the destination, even if there is a packet loss during transmission without retransmission. The receiver can recover back original packets at the destination. The recovery of lost packets is possible with the help of an encoding vector. The encoding vector provides sufficient information at the destination to know what coding coefficients are being used during the encoding process at the sender's side. Here I have successfully demonstrated 4 different concepts in RLNC. They are (i)Multicast sender-receiver (ii) Encode Decode using Random Coefficients (iii) Recoding (iv) Encode Decode on the fly. It is successfully implemented by using heterogeneous architecture and with the help of the Kodo network coding Library. Also, I contributed a survey on the state of the art techniques in RLNC and pointed out the advantages and disadvantages of it. The sliding window is yet another significant concept used in RLNC.
{"title":"Next Generation Network Coding Technique for IoT","authors":"G. Akilandeswary, J. Manickam","doi":"10.1109/ICCCNT49239.2020.9225314","DOIUrl":"https://doi.org/10.1109/ICCCNT49239.2020.9225314","url":null,"abstract":"Recoding is the unique feature of Random Linear Network Coding (RLNC). It helps to reduce the number of transmissions. It provides low delay. RLNC is a next-generation network coding technique. Its ability to recover packet at the destination, even if there is a packet loss during transmission without retransmission. The receiver can recover back original packets at the destination. The recovery of lost packets is possible with the help of an encoding vector. The encoding vector provides sufficient information at the destination to know what coding coefficients are being used during the encoding process at the sender's side. Here I have successfully demonstrated 4 different concepts in RLNC. They are (i)Multicast sender-receiver (ii) Encode Decode using Random Coefficients (iii) Recoding (iv) Encode Decode on the fly. It is successfully implemented by using heterogeneous architecture and with the help of the Kodo network coding Library. Also, I contributed a survey on the state of the art techniques in RLNC and pointed out the advantages and disadvantages of it. The sliding window is yet another significant concept used in RLNC.","PeriodicalId":6581,"journal":{"name":"2017 8th International Conference on Computing, Communication and Networking Technologies (ICCCNT)","volume":"88 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80148786","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 : 2020-07-01DOI: 10.1109/ICCCNT49239.2020.9225617
L. AleenaS., A. PrajithC.
Diabetic retinopathy is an eye disease, which causes blindness in diabetic patients. Early detection and periodic screening of diabetic retinopathy can reduce the progress of the disease and reduce vision loss. Due to diabetic retinopathy, there are lesions in the retina. Retinal lesions are mainly dark and bright lesions. Lesions have different properties such as colour, shape, and size. Microaneurysms (MAs) and hemorrhages (HEMs) are dark lesions and exudates(EXs) are bright lesions. This work proposes a retinal lesions detection system for screening diabetic retinopathy. Top hat and bottom hat transform are used for the enhancement of the image for the dark lesion detection. The optic disc is suppressed first to facilitate further processing of bright lesion detection. Morphological opening and closing are used for the detection of the bright lesion. Two SVMs are used in this work, one SVM is used to classify the dark lesion into microaneurysms and hemorrhages, and other one classify the bright lesion into hard exudates and non-hard exudates.
{"title":"Retinal Lesions Detection for Screening of Diabetic Retinopathy","authors":"L. AleenaS., A. PrajithC.","doi":"10.1109/ICCCNT49239.2020.9225617","DOIUrl":"https://doi.org/10.1109/ICCCNT49239.2020.9225617","url":null,"abstract":"Diabetic retinopathy is an eye disease, which causes blindness in diabetic patients. Early detection and periodic screening of diabetic retinopathy can reduce the progress of the disease and reduce vision loss. Due to diabetic retinopathy, there are lesions in the retina. Retinal lesions are mainly dark and bright lesions. Lesions have different properties such as colour, shape, and size. Microaneurysms (MAs) and hemorrhages (HEMs) are dark lesions and exudates(EXs) are bright lesions. This work proposes a retinal lesions detection system for screening diabetic retinopathy. Top hat and bottom hat transform are used for the enhancement of the image for the dark lesion detection. The optic disc is suppressed first to facilitate further processing of bright lesion detection. Morphological opening and closing are used for the detection of the bright lesion. Two SVMs are used in this work, one SVM is used to classify the dark lesion into microaneurysms and hemorrhages, and other one classify the bright lesion into hard exudates and non-hard exudates.","PeriodicalId":6581,"journal":{"name":"2017 8th International Conference on Computing, Communication and Networking Technologies (ICCCNT)","volume":"14 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74622105","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 : 2020-07-01DOI: 10.1109/ICCCNT49239.2020.9225493
K. VenkataravanaNayak, S. Sharathkumar, J. Arunalatha, R. VenugopalK.
Image retrieval plays an important role in the Digital imaging and media such as image classification, photography, medical imaging etc., in which the obtained information is crucial for the analysis of images. Extraction of representative features is a challenge due to the variations in geometric, photometric image features. The feature fusion process affords compact discriminative features of an image; this crucial information requires in analysing images accurately to increase the accuracy. Hence, Image Retrieval using feature fusion and Glowworm Swarm Optimization (IR-FF-GSO) is proposed. Multiple features are extracted with Texture, Color, Statistical and Scale Invariant Feature Transform (SIFT) descriptors to perform retrieval process. Feature vector is fused using optimized weight value which is obtained from GSO algorithm. The proposed method yields 95.5% retrieval accuracy on ImageNet database and is accurate compared to the conventional image retrieval method by over 10% [1].
{"title":"IR-FF-GSO: Image Retrieval using Feature Fusion and Glowworm Swarm Optimization","authors":"K. VenkataravanaNayak, S. Sharathkumar, J. Arunalatha, R. VenugopalK.","doi":"10.1109/ICCCNT49239.2020.9225493","DOIUrl":"https://doi.org/10.1109/ICCCNT49239.2020.9225493","url":null,"abstract":"Image retrieval plays an important role in the Digital imaging and media such as image classification, photography, medical imaging etc., in which the obtained information is crucial for the analysis of images. Extraction of representative features is a challenge due to the variations in geometric, photometric image features. The feature fusion process affords compact discriminative features of an image; this crucial information requires in analysing images accurately to increase the accuracy. Hence, Image Retrieval using feature fusion and Glowworm Swarm Optimization (IR-FF-GSO) is proposed. Multiple features are extracted with Texture, Color, Statistical and Scale Invariant Feature Transform (SIFT) descriptors to perform retrieval process. Feature vector is fused using optimized weight value which is obtained from GSO algorithm. The proposed method yields 95.5% retrieval accuracy on ImageNet database and is accurate compared to the conventional image retrieval method by over 10% [1].","PeriodicalId":6581,"journal":{"name":"2017 8th International Conference on Computing, Communication and Networking Technologies (ICCCNT)","volume":"63 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85585098","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 : 2020-07-01DOI: 10.1109/ICCCNT49239.2020.9225421
C. SujithaA., A. PrajithC.
Nowadays, underwater imaging has acquired considerable attention since it finds application in underwater object identification, species life monitoring, oil/gas pipeline detection, pollution monitoring etc. Attenuation of light is one of the major problems in capturing these images. Therefore, enhancement of underwater image is an important task. Visual features of the image can be improved by various image enhancement techniques. A useful method is to build up a fusion and dehazing technique which process a degraded input image. The proposed fusion- based underwater image enhancement technique aims to provide a final picture, which overcome all the insufficiencies of the input image by considering various weight maps. This can be achieved by employing white balancing on the underwater image followed by contrast stretching correction. Then sharpening, multi scale fusion process and dehazing can be performed to improve the visual appearance.
{"title":"Underwater Image Enhancement by Multiscale Fusion Technique and Dehazing","authors":"C. SujithaA., A. PrajithC.","doi":"10.1109/ICCCNT49239.2020.9225421","DOIUrl":"https://doi.org/10.1109/ICCCNT49239.2020.9225421","url":null,"abstract":"Nowadays, underwater imaging has acquired considerable attention since it finds application in underwater object identification, species life monitoring, oil/gas pipeline detection, pollution monitoring etc. Attenuation of light is one of the major problems in capturing these images. Therefore, enhancement of underwater image is an important task. Visual features of the image can be improved by various image enhancement techniques. A useful method is to build up a fusion and dehazing technique which process a degraded input image. The proposed fusion- based underwater image enhancement technique aims to provide a final picture, which overcome all the insufficiencies of the input image by considering various weight maps. This can be achieved by employing white balancing on the underwater image followed by contrast stretching correction. Then sharpening, multi scale fusion process and dehazing can be performed to improve the visual appearance.","PeriodicalId":6581,"journal":{"name":"2017 8th International Conference on Computing, Communication and Networking Technologies (ICCCNT)","volume":"34 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76089076","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 : 2020-07-01DOI: 10.1109/ICCCNT49239.2020.9225653
M. Bhavya, N. PushpaC., J. Thriveni, R. VenugopalK.
In the present era, cloud computing is built to provide many computation techniques and storage resources to the data user for later access. Data encryption is very important to ensure privacy before outsourcing it to the cloud server. Querying the cloud for encrypted data retrieval is a time-consuming process because of processing overhead and huge amount of data stored in cloud. In the existing system, the VPSearch scheme offers only verifiability of search results and privacy protection. It does not offer an efficient file uploading and index generation which consumes more time thereby slowing the searching process. It would be a challenging task to minimize the time to efficiently search on the cloud for a particular document. In order to overcome these challenges, we have proposed an efficient index generation scheme using tree based index technique with Greedy Depth-first search algorithm, that minimizes the file uploading and search time. The proposed EFUMS-Efficient File Upload and Mutli-Keyword Search over Encrypted Cloud Data scheme reduces the time taken to compute an index tree for all the files that are to be uploaded in a document and also helps to store the files in a structured tree format. This resulted in minimizing the document upload time and a faster and efficient data access using multi-keyword search.
{"title":"EFUMS: Efficient File Upload and Mutli-Keyword Search over Encrypted Cloud Data","authors":"M. Bhavya, N. PushpaC., J. Thriveni, R. VenugopalK.","doi":"10.1109/ICCCNT49239.2020.9225653","DOIUrl":"https://doi.org/10.1109/ICCCNT49239.2020.9225653","url":null,"abstract":"In the present era, cloud computing is built to provide many computation techniques and storage resources to the data user for later access. Data encryption is very important to ensure privacy before outsourcing it to the cloud server. Querying the cloud for encrypted data retrieval is a time-consuming process because of processing overhead and huge amount of data stored in cloud. In the existing system, the VPSearch scheme offers only verifiability of search results and privacy protection. It does not offer an efficient file uploading and index generation which consumes more time thereby slowing the searching process. It would be a challenging task to minimize the time to efficiently search on the cloud for a particular document. In order to overcome these challenges, we have proposed an efficient index generation scheme using tree based index technique with Greedy Depth-first search algorithm, that minimizes the file uploading and search time. The proposed EFUMS-Efficient File Upload and Mutli-Keyword Search over Encrypted Cloud Data scheme reduces the time taken to compute an index tree for all the files that are to be uploaded in a document and also helps to store the files in a structured tree format. This resulted in minimizing the document upload time and a faster and efficient data access using multi-keyword search.","PeriodicalId":6581,"journal":{"name":"2017 8th International Conference on Computing, Communication and Networking Technologies (ICCCNT)","volume":"2 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85101708","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 : 2020-07-01DOI: 10.1109/ICCCNT49239.2020.9225477
P. AsifAhamad, S. Jeevaraj
In today's world with the increase in internet usage, every digital gadget is getting connected to the internet. Due to the open connectivity of the internet, devices connected to the internet are exposed to the disturbances caused by masqueraders, misfeasors, malware writers, and intruders. Researchers are in the continuous search of methods to detect the attacks in the network and to introduce these methods to the low computation capability devices. In this work, it aimed at creating a disruption sensing system for identifying the disturbances caused by intruders. It will be achieved by choosing the best traffic capturing component. Then, by introducing/improving the association rule mining algorithm to identify the patterns in the data and to generate better rules. Then, by using the proposed method based on fuzzy logic and inference system to help in identifying the attack.
{"title":"Intrusion classification using ECLAT and Fuzzy Logic","authors":"P. AsifAhamad, S. Jeevaraj","doi":"10.1109/ICCCNT49239.2020.9225477","DOIUrl":"https://doi.org/10.1109/ICCCNT49239.2020.9225477","url":null,"abstract":"In today's world with the increase in internet usage, every digital gadget is getting connected to the internet. Due to the open connectivity of the internet, devices connected to the internet are exposed to the disturbances caused by masqueraders, misfeasors, malware writers, and intruders. Researchers are in the continuous search of methods to detect the attacks in the network and to introduce these methods to the low computation capability devices. In this work, it aimed at creating a disruption sensing system for identifying the disturbances caused by intruders. It will be achieved by choosing the best traffic capturing component. Then, by introducing/improving the association rule mining algorithm to identify the patterns in the data and to generate better rules. Then, by using the proposed method based on fuzzy logic and inference system to help in identifying the attack.","PeriodicalId":6581,"journal":{"name":"2017 8th International Conference on Computing, Communication and Networking Technologies (ICCCNT)","volume":"39 1","pages":"1-7"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82245031","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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