Pub Date : 2018-02-01DOI: 10.1109/ICCMC.2018.8487788
Raisha Shrestha, S. Pradhan, Rahul Karn, S. Shrestha
In Maximum number of educational institutions we can see prevailing system of attendance where attendance of students are taken manually by the professors calling out the names of the students. In some universities we can find RFID system present for attendance. The manual system of attendance is very time consuming and may not be much efficient as well. Whereas RFID based attendance is also not much reliable as we don't know if the RFID card is actually used by the student whom it belongs or not. Both existing techniques for attendance system have problems in it.So our paper has used Image Processing techniques and automated the attendance system where the attendance is taken by the system by recognizing the faces of the students. The system has dataset of known faces or students such that when any unknown face detected inside the classroom, he/she will be recognized as an intruder. This will safeguard the students from any kind of invasion or attack. In this paper we have discussed the techniques which can be used to implement image processing for automating the attendance system and assure security of the students.
{"title":"Attendance and Security Assurance using Image Processing","authors":"Raisha Shrestha, S. Pradhan, Rahul Karn, S. Shrestha","doi":"10.1109/ICCMC.2018.8487788","DOIUrl":"https://doi.org/10.1109/ICCMC.2018.8487788","url":null,"abstract":"In Maximum number of educational institutions we can see prevailing system of attendance where attendance of students are taken manually by the professors calling out the names of the students. In some universities we can find RFID system present for attendance. The manual system of attendance is very time consuming and may not be much efficient as well. Whereas RFID based attendance is also not much reliable as we don't know if the RFID card is actually used by the student whom it belongs or not. Both existing techniques for attendance system have problems in it.So our paper has used Image Processing techniques and automated the attendance system where the attendance is taken by the system by recognizing the faces of the students. The system has dataset of known faces or students such that when any unknown face detected inside the classroom, he/she will be recognized as an intruder. This will safeguard the students from any kind of invasion or attack. In this paper we have discussed the techniques which can be used to implement image processing for automating the attendance system and assure security of the students.","PeriodicalId":6604,"journal":{"name":"2018 Second International Conference on Computing Methodologies and Communication (ICCMC)","volume":"35 1","pages":"544-548"},"PeriodicalIF":0.0,"publicationDate":"2018-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85624140","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 : 2018-02-01DOI: 10.1109/ICCMC.2018.8487992
Shivangi Gheewala, Rakesh Patel
Online social networks (OSNs) are emerging communication medium for people to establish and manage social relationships. In OSNs, regularly billions of users are involved in social interaction, content and opinion dissemination, networking, recommendations, scouting, alerting, and social campaigns. The popularization of OSNs open up a new perspectives and challenges to the study of social networks, being of interest to many fields. Social network is a place where social activities, business oriented activities, entertainment, and information are exchanged. It establish a worldwide connectivity environment where communities of people share their interests and activities, or who are interested in interests and activities of others Although social network has given immense benefits to people at the same time harming people with various mischievous activities that take place on social platforms. This causes significant economic loss to our society and even threaten the national security. All the social networks Facebook, Twitter, LinkedIn, etc. are highly susceptible to malware activities. Twitter is one of the biggest microblogging networking platform, it has more than half a billion tweets are posted every day in average by millions of users on Twitter. Such a versatility and wide spread of use, Twitter easily get intruded with malicious activities. Malicious activities includes malware intrusion, spam distribution, social attacks, etc. Spammers use social engineering attack strategy to send spam tweets, spam URLs, etc. This made twitter an ideal arena for proliferation of anomalous spam accounts. The impact stimulates researchers to develop a model that analyze, detects and recovers from defamatory actions in twitter. Twitter network is inundated with tens of millions of fake spam profiles which may jeopardize the normal user’s security and privacy. To improve real users safety and identification of spam profiles become key parts of the research.
{"title":"Machine Learning Based Twitter Spam Account Detection: A Review","authors":"Shivangi Gheewala, Rakesh Patel","doi":"10.1109/ICCMC.2018.8487992","DOIUrl":"https://doi.org/10.1109/ICCMC.2018.8487992","url":null,"abstract":"Online social networks (OSNs) are emerging communication medium for people to establish and manage social relationships. In OSNs, regularly billions of users are involved in social interaction, content and opinion dissemination, networking, recommendations, scouting, alerting, and social campaigns. The popularization of OSNs open up a new perspectives and challenges to the study of social networks, being of interest to many fields. Social network is a place where social activities, business oriented activities, entertainment, and information are exchanged. It establish a worldwide connectivity environment where communities of people share their interests and activities, or who are interested in interests and activities of others Although social network has given immense benefits to people at the same time harming people with various mischievous activities that take place on social platforms. This causes significant economic loss to our society and even threaten the national security. All the social networks Facebook, Twitter, LinkedIn, etc. are highly susceptible to malware activities. Twitter is one of the biggest microblogging networking platform, it has more than half a billion tweets are posted every day in average by millions of users on Twitter. Such a versatility and wide spread of use, Twitter easily get intruded with malicious activities. Malicious activities includes malware intrusion, spam distribution, social attacks, etc. Spammers use social engineering attack strategy to send spam tweets, spam URLs, etc. This made twitter an ideal arena for proliferation of anomalous spam accounts. The impact stimulates researchers to develop a model that analyze, detects and recovers from defamatory actions in twitter. Twitter network is inundated with tens of millions of fake spam profiles which may jeopardize the normal user’s security and privacy. To improve real users safety and identification of spam profiles become key parts of the research.","PeriodicalId":6604,"journal":{"name":"2018 Second International Conference on Computing Methodologies and Communication (ICCMC)","volume":"42 3","pages":"79-84"},"PeriodicalIF":0.0,"publicationDate":"2018-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91423077","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 : 2018-02-01DOI: 10.1109/ICCMC.2018.8487531
Mamta Patil, H. K. Chavan
Everyday large volumes of data are produced. Millions of users share and dissipate most up-to-date information on twitter. Traditional text mining suffers severely from short and noisy nature of tweets. Event detection from twitter data has many new challenges when compared to event detection from traditional media. Noisy nature and limited length are the challenges imposed by twitter data. Event detection performance on twitter is negatively affected by nature of tweets. This paper proposes SegAnalysis framework to tackle these challenges. It performs tweet segmentation, event detection and sentiment analysis. Tweet segmentation is performed in a batch mode using POS (part of speech) tagger on recent online tweets fetched by the user. Segmentation of a tweet preserves the named entities and its stickiness score is calculated. Naïve Bayes classification and online clustering detect events. These events improve situational awareness and decision support. Sentiment analysis categorizes tweets as positive, negative and neutral depending on sentiment score of a tweet. SegAnalysis framework can be extended to deal with events belonging to multiple clusters.
{"title":"Event Based Sentiment Analysis of Twitter Data","authors":"Mamta Patil, H. K. Chavan","doi":"10.1109/ICCMC.2018.8487531","DOIUrl":"https://doi.org/10.1109/ICCMC.2018.8487531","url":null,"abstract":"Everyday large volumes of data are produced. Millions of users share and dissipate most up-to-date information on twitter. Traditional text mining suffers severely from short and noisy nature of tweets. Event detection from twitter data has many new challenges when compared to event detection from traditional media. Noisy nature and limited length are the challenges imposed by twitter data. Event detection performance on twitter is negatively affected by nature of tweets. This paper proposes SegAnalysis framework to tackle these challenges. It performs tweet segmentation, event detection and sentiment analysis. Tweet segmentation is performed in a batch mode using POS (part of speech) tagger on recent online tweets fetched by the user. Segmentation of a tweet preserves the named entities and its stickiness score is calculated. Naïve Bayes classification and online clustering detect events. These events improve situational awareness and decision support. Sentiment analysis categorizes tweets as positive, negative and neutral depending on sentiment score of a tweet. SegAnalysis framework can be extended to deal with events belonging to multiple clusters.","PeriodicalId":6604,"journal":{"name":"2018 Second International Conference on Computing Methodologies and Communication (ICCMC)","volume":"31 1","pages":"1050-1054"},"PeriodicalIF":0.0,"publicationDate":"2018-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78862843","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 : 2018-02-01DOI: 10.1109/ICCMC.2018.8487695
Shreyal Gajare, S. Sonawani
With the recent advances in machines & technology, there is revolution in healthcare industry also. Thus, it gave rise to the concept of Electronic Health Record (EHR) which stores patients demographics, lab tests & results, medical history, habits etc. in electronic form. EHR is voluminous data which is difficult to store, maintain or alter. To provide extended life for people, health risk prediction model using this EHR is formulated in this work. Feature Selection is used to select only associated or relevant data from the dataset. Logistic Regression is used with improved loss functionality parameter which increases the accuracy, response time and performance of the system. Representation Learning enables formation of feature vector of the selected features thus calculating their scores. Further, risk prediction is performed by the neural network model. Deep Neural Network (DNN) is used with many hidden layers containing activation functions. Transfer learning is used to avoid re-training of the whole system every time new data enters the model. Dataset used here is of hypertension. EHR dataset is also synthetically created for analysis.
{"title":"Improved Automatic Feature Selection Approach for Health Risk Prediction","authors":"Shreyal Gajare, S. Sonawani","doi":"10.1109/ICCMC.2018.8487695","DOIUrl":"https://doi.org/10.1109/ICCMC.2018.8487695","url":null,"abstract":"With the recent advances in machines & technology, there is revolution in healthcare industry also. Thus, it gave rise to the concept of Electronic Health Record (EHR) which stores patients demographics, lab tests & results, medical history, habits etc. in electronic form. EHR is voluminous data which is difficult to store, maintain or alter. To provide extended life for people, health risk prediction model using this EHR is formulated in this work. Feature Selection is used to select only associated or relevant data from the dataset. Logistic Regression is used with improved loss functionality parameter which increases the accuracy, response time and performance of the system. Representation Learning enables formation of feature vector of the selected features thus calculating their scores. Further, risk prediction is performed by the neural network model. Deep Neural Network (DNN) is used with many hidden layers containing activation functions. Transfer learning is used to avoid re-training of the whole system every time new data enters the model. Dataset used here is of hypertension. EHR dataset is also synthetically created for analysis.","PeriodicalId":6604,"journal":{"name":"2018 Second International Conference on Computing Methodologies and Communication (ICCMC)","volume":"10 1","pages":"816-819"},"PeriodicalIF":0.0,"publicationDate":"2018-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78401649","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 : 2018-02-01DOI: 10.1109/ICCMC.2018.8487969
R. Devika, S. Revathy, Sai surriya Priyanka U, V. Subramaniya swamy
Social networks and online news services are used by users to communicate and share messages. One such social network is Twitter. Earlier its messages were restricted to 140 characters, but from November 7, 2017 its limit was extended to 280 characters except Japanese, Korean and Chinese languages. Because of restricted characters used, it is famously called micro blogging. Mining twitter data has become popular, because it provides useful information which is being used in various fields. This paper highlights various clustering techniques that can be used in twitter data mining with advantages and limitation.
{"title":"Survey on clustering techniques in Twitter data","authors":"R. Devika, S. Revathy, Sai surriya Priyanka U, V. Subramaniya swamy","doi":"10.1109/ICCMC.2018.8487969","DOIUrl":"https://doi.org/10.1109/ICCMC.2018.8487969","url":null,"abstract":"Social networks and online news services are used by users to communicate and share messages. One such social network is Twitter. Earlier its messages were restricted to 140 characters, but from November 7, 2017 its limit was extended to 280 characters except Japanese, Korean and Chinese languages. Because of restricted characters used, it is famously called micro blogging. Mining twitter data has become popular, because it provides useful information which is being used in various fields. This paper highlights various clustering techniques that can be used in twitter data mining with advantages and limitation.","PeriodicalId":6604,"journal":{"name":"2018 Second International Conference on Computing Methodologies and Communication (ICCMC)","volume":"20 1","pages":"1073-1077"},"PeriodicalIF":0.0,"publicationDate":"2018-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78581097","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 : 2018-02-01DOI: 10.1109/ICCMC.2018.8487238
R. Kanmani, V. Uma
Recommendation System provides the user with the interesting materials which are extracted from their preference. For simplifying the information retrieval and in order to provide the user with preferred result with more accuracy, recommendation system is being used. Recommendation based services are also used in social networks such as Facebook, Twitter, Instagram etc. Geo-tagged data plays a major role in case of recommendation systems as they will be providing recommendations with respect to the users locations. The semantic classification of the location is done using Support Vector Machine. By considering the location co-ordinates the nearest possible travel routes are identified by Google Maps and the shorter distance are computed using k-Nearest Neighbour. In this work, recommendation of products is given by means of considering the frequent buying pattern of the user using Prefix span algorithm, similar users ratings computed by Collaborative Filtering and the popular items available on the travel route. The proposed system has been implemented and evaluated.
{"title":"Identification Of User Preference by Sequential Pattern Mining and Recommendation of Products using Geo-tagged data","authors":"R. Kanmani, V. Uma","doi":"10.1109/ICCMC.2018.8487238","DOIUrl":"https://doi.org/10.1109/ICCMC.2018.8487238","url":null,"abstract":"Recommendation System provides the user with the interesting materials which are extracted from their preference. For simplifying the information retrieval and in order to provide the user with preferred result with more accuracy, recommendation system is being used. Recommendation based services are also used in social networks such as Facebook, Twitter, Instagram etc. Geo-tagged data plays a major role in case of recommendation systems as they will be providing recommendations with respect to the users locations. The semantic classification of the location is done using Support Vector Machine. By considering the location co-ordinates the nearest possible travel routes are identified by Google Maps and the shorter distance are computed using k-Nearest Neighbour. In this work, recommendation of products is given by means of considering the frequent buying pattern of the user using Prefix span algorithm, similar users ratings computed by Collaborative Filtering and the popular items available on the travel route. The proposed system has been implemented and evaluated.","PeriodicalId":6604,"journal":{"name":"2018 Second International Conference on Computing Methodologies and Communication (ICCMC)","volume":"20 1","pages":"108-113"},"PeriodicalIF":0.0,"publicationDate":"2018-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72870104","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 : 2018-02-01DOI: 10.1109/ICCMC.2018.8487835
Preeti, Dinesh Kumar
Face recognition applications are gaining popularity day by day. Feature extraction, selection, and recognition are the three main steps of face recognition system. Recognition is done using classifiers as these play a vital role in making the system recognize the faces accurately to the extent possible. This paper evaluates the performance of the system using four different distance classifiers over ORL databases. DCT (Discrete Cosine Transform)-PCA (Principal Component Analysis) and LDA (Linear Discriminate Analysis) methods followed by Cuckoo Search algorithm have been used for extraction and selection of important features respectively. The results demonstrate the efficiency and efficacy of the face recognition system upon using Euclidean distance classifier.
{"title":"Performance Evaluation of Face Recognition System using various Distance Classifiers","authors":"Preeti, Dinesh Kumar","doi":"10.1109/ICCMC.2018.8487835","DOIUrl":"https://doi.org/10.1109/ICCMC.2018.8487835","url":null,"abstract":"Face recognition applications are gaining popularity day by day. Feature extraction, selection, and recognition are the three main steps of face recognition system. Recognition is done using classifiers as these play a vital role in making the system recognize the faces accurately to the extent possible. This paper evaluates the performance of the system using four different distance classifiers over ORL databases. DCT (Discrete Cosine Transform)-PCA (Principal Component Analysis) and LDA (Linear Discriminate Analysis) methods followed by Cuckoo Search algorithm have been used for extraction and selection of important features respectively. The results demonstrate the efficiency and efficacy of the face recognition system upon using Euclidean distance classifier.","PeriodicalId":6604,"journal":{"name":"2018 Second International Conference on Computing Methodologies and Communication (ICCMC)","volume":"27 1","pages":"322-327"},"PeriodicalIF":0.0,"publicationDate":"2018-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84977551","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 : 2018-02-01DOI: 10.1109/ICCMC.2018.8487483
Jasmina N Vanasiwala, Nirali R. Nanavati
The advances in digital information applications facilitates the collection of huge amount of data about governments, healthcare, other organizations and individuals. To make this data available for researchers, businesses and other users, it needs to be released. This in turn increases the demand of exchanging and publishing this collected data. However, data in its original form, typically contains sensitive information about individuals and/or organizations, and publishing such data will violate individual or organizational privacy. Hence, Privacy Preserving Data Publishing (PPDP) provides methods and tools for publishing useful information while preserving data privacy. Before data is published to the concerned users, it is altered to maintain its privacy without compromising data utility, using various anonymization techniques. Real-time datasets contain different types of Multiple Sensitive Attributes (MSAs) (which could be numerical or categorical). Anonymization for only Single Sensitive Attribute is not suitable for functional usage. Thus, it is important to maintain the association between these MSAs and to preserve the privacy of Mixed (numerical and categorical) MSAs efficiently while working with high dimensional data. The main focus of this paper is to analyse the different schemes proposed in literature for PPDP of MSAs.
{"title":"Multiple Sensitive Attributes Based Privacy Preserving Data Publishing","authors":"Jasmina N Vanasiwala, Nirali R. Nanavati","doi":"10.1109/ICCMC.2018.8487483","DOIUrl":"https://doi.org/10.1109/ICCMC.2018.8487483","url":null,"abstract":"The advances in digital information applications facilitates the collection of huge amount of data about governments, healthcare, other organizations and individuals. To make this data available for researchers, businesses and other users, it needs to be released. This in turn increases the demand of exchanging and publishing this collected data. However, data in its original form, typically contains sensitive information about individuals and/or organizations, and publishing such data will violate individual or organizational privacy. Hence, Privacy Preserving Data Publishing (PPDP) provides methods and tools for publishing useful information while preserving data privacy. Before data is published to the concerned users, it is altered to maintain its privacy without compromising data utility, using various anonymization techniques. Real-time datasets contain different types of Multiple Sensitive Attributes (MSAs) (which could be numerical or categorical). Anonymization for only Single Sensitive Attribute is not suitable for functional usage. Thus, it is important to maintain the association between these MSAs and to preserve the privacy of Mixed (numerical and categorical) MSAs efficiently while working with high dimensional data. The main focus of this paper is to analyse the different schemes proposed in literature for PPDP of MSAs.","PeriodicalId":6604,"journal":{"name":"2018 Second International Conference on Computing Methodologies and Communication (ICCMC)","volume":"22 1","pages":"394-400"},"PeriodicalIF":0.0,"publicationDate":"2018-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85396597","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 : 2018-02-01DOI: 10.1109/ICCMC.2018.8487752
V. Sanju, G. Venu, Reenu Sara Joseph, M. Stephen, Melvin Mathew
Wireless networks are currently dominating over wired networks in many applications.Mobility and scalability are some of the features that has led to make wireless networks more popular. Out of the many wireless network Mobile Ad-Hoc network (MANET) are the most popular. They have a highly dynamic and random topology. Essentially the ad hoc network is a collection of nodes communicating with each other by forming a multi-hop network. MANET is used in many military applications because of its self configuring nature. However Mobile Ad-Hoc Network (MANET) has much vulnerability towards security attacks due to its features of open medium, limited physical security, dynamic changing topology, lack of centralized monitoring and organization point.
{"title":"EAACK - Enhanced Adaptive Acknowledgment","authors":"V. Sanju, G. Venu, Reenu Sara Joseph, M. Stephen, Melvin Mathew","doi":"10.1109/ICCMC.2018.8487752","DOIUrl":"https://doi.org/10.1109/ICCMC.2018.8487752","url":null,"abstract":"Wireless networks are currently dominating over wired networks in many applications.Mobility and scalability are some of the features that has led to make wireless networks more popular. Out of the many wireless network Mobile Ad-Hoc network (MANET) are the most popular. They have a highly dynamic and random topology. Essentially the ad hoc network is a collection of nodes communicating with each other by forming a multi-hop network. MANET is used in many military applications because of its self configuring nature. However Mobile Ad-Hoc Network (MANET) has much vulnerability towards security attacks due to its features of open medium, limited physical security, dynamic changing topology, lack of centralized monitoring and organization point.","PeriodicalId":6604,"journal":{"name":"2018 Second International Conference on Computing Methodologies and Communication (ICCMC)","volume":"118 1","pages":"140-148"},"PeriodicalIF":0.0,"publicationDate":"2018-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87617771","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 : 2018-02-01DOI: 10.1109/ICCMC.2018.8487535
R. Katarya, Chhavi Jain
Mobile systems and applications face a number of vulnerabilities that can lead to a breach of confidentiality of information. Users these days rely more on mobile systems and various applications for their day to day activities. Different applications can pose different risks to the security of mobile systems and can sometimes become the cause of other vulnerabilities as well. This paper presents and reviews risk analysis done at various levels in mobile systems, namely, the static analysis layer, dynamic analysis layer and the behavioral analysis layer. Risk can propagate through these layers and various techniques and approaches have been proposed to evaluate such potential risks. These strategies can help improve the security of mobile applications as well as the entire mobile applications.
{"title":"Multilayered Risk analysis of Mobile systems and Apps","authors":"R. Katarya, Chhavi Jain","doi":"10.1109/ICCMC.2018.8487535","DOIUrl":"https://doi.org/10.1109/ICCMC.2018.8487535","url":null,"abstract":"Mobile systems and applications face a number of vulnerabilities that can lead to a breach of confidentiality of information. Users these days rely more on mobile systems and various applications for their day to day activities. Different applications can pose different risks to the security of mobile systems and can sometimes become the cause of other vulnerabilities as well. This paper presents and reviews risk analysis done at various levels in mobile systems, namely, the static analysis layer, dynamic analysis layer and the behavioral analysis layer. Risk can propagate through these layers and various techniques and approaches have been proposed to evaluate such potential risks. These strategies can help improve the security of mobile applications as well as the entire mobile applications.","PeriodicalId":6604,"journal":{"name":"2018 Second International Conference on Computing Methodologies and Communication (ICCMC)","volume":"20 1","pages":"64-67"},"PeriodicalIF":0.0,"publicationDate":"2018-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81888954","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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