Pub Date : 2019-08-01DOI: 10.22042/ISECURE.2019.11.0.19
Anwar Saeed, Muhammad Yousif, A. Fatima, Sagheer Abbas, Muhammad Adnan Khan, Leena Anum, Ali Akram
With the innovation of cloud computing industry lots of services were provided based on different deployment criteria. Nowadays everyone tries to remain connected and demand maximum utilization of resources with minimum timeand effort. Thus, making it an important challenge in cloud computing for optimum utilization of resources. To overcome this issue, many techniques have been proposed shill no comprehensive results have been achieved. Cloud Computing offers elastic and scalable resource sharing services by using resource management. In this article, a hybrid approach has been proposed with an objective to achieve the maximum resource utilization. In this proposed method, adaptive back propagation neural network and multi-level priority-based scheduling are being carried out for optimum resource utilization. This hybrid technique will improve the utilization of resources in cloud computing. This shows result in simulation-based on the form of MSE and Regression with job dataset, on behalf of the comparison of three algorithms like Scaled Conjugate Gradient (SCG), Levenberg Marquardt (LM) and Bayesian Regularization (BR). BR gives a better result with 60 hidden layers Neurons to other algorithms. BR gives 2.05 MSE and 95.8 regressions in Validation, LM gives 2.91 MSE and 94.06 regressions with this and SCG gives 3.92 MSE and 91.85 regressions.
{"title":"An Optimal Utilization of Cloud Resources using Adaptive Back Propagation Neural Network and Multi-Level Priority Queue Scheduling","authors":"Anwar Saeed, Muhammad Yousif, A. Fatima, Sagheer Abbas, Muhammad Adnan Khan, Leena Anum, Ali Akram","doi":"10.22042/ISECURE.2019.11.0.19","DOIUrl":"https://doi.org/10.22042/ISECURE.2019.11.0.19","url":null,"abstract":"With the innovation of cloud computing industry lots of services were provided based on different deployment criteria. Nowadays everyone tries to remain connected and demand maximum utilization of resources with minimum timeand effort. Thus, making it an important challenge in cloud computing for optimum utilization of resources. To overcome this issue, many techniques have been proposed shill no comprehensive results have been achieved. Cloud Computing offers elastic and scalable resource sharing services by using resource management. In this article, a hybrid approach has been proposed with an objective to achieve the maximum resource utilization. In this proposed method, adaptive back propagation neural network and multi-level priority-based scheduling are being carried out for optimum resource utilization. This hybrid technique will improve the utilization of resources in cloud computing. This shows result in simulation-based on the form of MSE and Regression with job dataset, on behalf of the comparison of three algorithms like Scaled Conjugate Gradient (SCG), Levenberg Marquardt (LM) and Bayesian Regularization (BR). BR gives a better result with 60 hidden layers Neurons to other algorithms. BR gives 2.05 MSE and 95.8 regressions in Validation, LM gives 2.91 MSE and 94.06 regressions with this and SCG gives 3.92 MSE and 91.85 regressions.","PeriodicalId":436674,"journal":{"name":"ISC Int. J. Inf. Secur.","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125785350","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 : 2019-08-01DOI: 10.22042/ISECURE.2019.11.0.18
S. A. Alahmari
The increasing volatility in pricing and growing potential for profit in digital currency have made predicting the price of cryptocurrency a very attractive research topic. Several studies have already been conducted using various machine-learning models to predict crypto currency prices. This study presented in this paper applied a classic Autoregressive Integrated Moving Average(ARIMA) model to predict the prices of the three major cryptocurrencies âAT Bitcoin, XRP and Ethereum âAT using daily, weekly and monthly time series. The results demonstrated that ARIMA outperforms most other methods in predicting cryptocurrency prices on a daily time series basis in terms of mean absolute error (MAE), mean squared error (MSE) and root mean squared error(RMSE).
{"title":"Using Machine Learning ARIMA to Predict the Price of Cryptocurrencies","authors":"S. A. Alahmari","doi":"10.22042/ISECURE.2019.11.0.18","DOIUrl":"https://doi.org/10.22042/ISECURE.2019.11.0.18","url":null,"abstract":"The increasing volatility in pricing and growing potential for profit in digital currency have made predicting the price of cryptocurrency a very attractive research topic. Several studies have already been conducted using various machine-learning models to predict crypto currency prices. This study presented in this paper applied a classic Autoregressive Integrated Moving Average(ARIMA) model to predict the prices of the three major cryptocurrencies âAT Bitcoin, XRP and Ethereum âAT using daily, weekly and monthly time series. The results demonstrated that ARIMA outperforms most other methods in predicting cryptocurrency prices on a daily time series basis in terms of mean absolute error (MAE), mean squared error (MSE) and root mean squared error(RMSE).","PeriodicalId":436674,"journal":{"name":"ISC Int. J. Inf. Secur.","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133372111","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 : 2019-08-01DOI: 10.22042/isecure.2019.11.3.23
Istabraq M. Al-Joboury, E. Al-Hemiary
The Internet of Things (IoT) becomes the future of a global data field in which the embedded devices communicate with each other, exchange data and making decisions through the Internet. IoT could improve the quality of life in smart cities, but a massive amount of data from different smart devices could slow down or crash database systems. In addition, IoT data transfer to Cloud for monitoring information and generating feedback that will lead to high delay in infrastructure level. Fog Computing can help by offering services closer to edge devices. In this paper, we propose an efficient system architecture to mitigate the problem of delay. We provide performance analysis like response time, throughput and packet loss for MQTT (Message Queue Telemetry Transport) and HTTP (Hyper Text Transfer Protocol) protocols based on Cloud or Fog servers with large volume of data from emulated traffic generator working alongside one real sensor . We implement both protocols in the same architecture, with low cost embedded devices to local and Cloud servers with different platforms. The results show that HTTP response time is 12.1 and 4.76 times higher than MQTT Fog and Cloud based located in the same geographical area of the sensors respectively. The worst case in performance is observed when the Cloud is public and outside the country region. The results obtained for throughput shows that MQTT has the capability to carry the data with available bandwidth and lowest percentage of packet loss. We also prove that the proposed Fog architecture is an efficient way to reduce latency and enhance performance in Cloud based IoT.
{"title":"IoT Protocols Based Fog/Cloud over High Traffic","authors":"Istabraq M. Al-Joboury, E. Al-Hemiary","doi":"10.22042/isecure.2019.11.3.23","DOIUrl":"https://doi.org/10.22042/isecure.2019.11.3.23","url":null,"abstract":"The Internet of Things (IoT) becomes the future of a global data field in which the embedded devices communicate with each other, exchange data and making decisions through the Internet. IoT could improve the quality of life in smart cities, but a massive amount of data from different smart devices could slow down or crash database systems. In addition, IoT data transfer to Cloud for monitoring information and generating feedback that will lead to high delay in infrastructure level. Fog Computing can help by offering services closer to edge devices. In this paper, we propose an efficient system architecture to mitigate the problem of delay. We provide performance analysis like response time, throughput and packet loss for MQTT (Message Queue Telemetry Transport) and HTTP (Hyper Text Transfer Protocol) protocols based on Cloud or Fog servers with large volume of data from emulated traffic generator working alongside one real sensor . We implement both protocols in the same architecture, with low cost embedded devices to local and Cloud servers with different platforms. The results show that HTTP response time is 12.1 and 4.76 times higher than MQTT Fog and Cloud based located in the same geographical area of the sensors respectively. The worst case in performance is observed when the Cloud is public and outside the country region. The results obtained for throughput shows that MQTT has the capability to carry the data with available bandwidth and lowest percentage of packet loss. We also prove that the proposed Fog architecture is an efficient way to reduce latency and enhance performance in Cloud based IoT.","PeriodicalId":436674,"journal":{"name":"ISC Int. J. Inf. Secur.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114315762","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 : 2019-08-01DOI: 10.22042/ISECURE.2019.11.0.10
F. Córdova, C. Durán, F. Palominos
Port organizations have focused their efforts on physical or tangible assets, generating profitability and value. However, it is recognized that the greatest sustainable competitive advantage is the creation of knowledge using the intangible assets of the organization. The Balanced ScoreCard, as a performance tool, has incorporated intangible assets such as intellectual, structural and social capital into management. In this way, the port community can count on new forms of managing innovation, strengthening organizational practices, and increasing collaborative work teams. In this study, the concepts from analysis of the cognitive SWOT are applied to diagnose the port activity and its community. In workshops with experts and from the vision, mission, cognitive SWOT and strategies, a cognitive strategic map considering strategic objectives and indicators is designed in the customer, processes, and learning and growth axis for the port and port community. Causal relationships between objectives, associated indicators and incidence factors are established in a forward way from learning and growth axis to customer axis. Then, the incidence matrix is developed and the direct and indirect effects between factors are analyzed, which allows recommending the future course of the port and its community.
{"title":"Cognitive Strategic Model applied to a Port System","authors":"F. Córdova, C. Durán, F. Palominos","doi":"10.22042/ISECURE.2019.11.0.10","DOIUrl":"https://doi.org/10.22042/ISECURE.2019.11.0.10","url":null,"abstract":"Port organizations have focused their efforts on physical or tangible assets, generating profitability and value. However, it is recognized that the greatest sustainable competitive advantage is the creation of knowledge using the intangible assets of the organization. The Balanced ScoreCard, as a performance tool, has incorporated intangible assets such as intellectual, structural and social capital into management. In this way, the port community can count on new forms of managing innovation, strengthening organizational practices, and increasing collaborative work teams. In this study, the concepts from analysis of the cognitive SWOT are applied to diagnose the port activity and its community. In workshops with experts and from the vision, mission, cognitive SWOT and strategies, a cognitive strategic map considering strategic objectives and indicators is designed in the customer, processes, and learning and growth axis for the port and port community. Causal relationships between objectives, associated indicators and incidence factors are established in a forward way from learning and growth axis to customer axis. Then, the incidence matrix is developed and the direct and indirect effects between factors are analyzed, which allows recommending the future course of the port and its community.","PeriodicalId":436674,"journal":{"name":"ISC Int. J. Inf. Secur.","volume":"92 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134395725","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 : 2019-08-01DOI: 10.22042/ISECURE.2019.11.0.3
Ibrahim Soran, Qing Tan
In the recent years, social networks (SN) are now employed for communication and networking, socializing, marketing, as well as one’s daily life. Billions of people in the world are connected though various SN platforms and applications, which results in generating massive amount of data online. This includes personal data or Personally Identifiable Information (PII). While more and more data are collected about users by different organizations and companies, privacy concerns on the SNs have become more and more prominent. In this paper, we present a study on information privacy in SNs through exploring the general laws and regulations on collecting, using and disclosure of information from Canadian perspectives based on the Personal Information Protection and Electronic Document Act (PIPEDA). The main focus of this paper is to present results from a survey and the findings of the survey.
{"title":"A Sudy on Information Privacy Issue on Social Networks","authors":"Ibrahim Soran, Qing Tan","doi":"10.22042/ISECURE.2019.11.0.3","DOIUrl":"https://doi.org/10.22042/ISECURE.2019.11.0.3","url":null,"abstract":"In the recent years, social networks (SN) are now employed for communication and networking, socializing, marketing, as well as one’s daily life. Billions of people in the world are connected though various SN platforms and applications, which results in generating massive amount of data online. This includes personal data or Personally Identifiable Information (PII). While more and more data are collected about users by different organizations and companies, privacy concerns on the SNs have become more and more prominent. In this paper, we present a study on information privacy in SNs through exploring the general laws and regulations on collecting, using and disclosure of information from Canadian perspectives based on the Personal Information Protection and Electronic Document Act (PIPEDA). The main focus of this paper is to present results from a survey and the findings of the survey.","PeriodicalId":436674,"journal":{"name":"ISC Int. J. Inf. Secur.","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130038554","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 : 2019-08-01DOI: 10.22042/ISECURE.2019.11.0.11
Ahmed Banimustafa
This paper presents a data mining application in metabolomics. It aims at building an enhanced machine learning classifier that can be used for diagnosing cachexia syndrome and identifying its involved biomarkers. To achieve this goal, a data-driven analysis is carried out using a public dataset consisting of 1H-NMR metabolite profile. This dataset suffers from the problem of imbalanced classes which is known to deteriorate the performance of classifiers. It also influences its validity and generalizablity. The classification models in this study were built using five machine learning algorithms known as PLS-DA, MLP, SVM, C4.5 and ID3. This model is built after carrying out a number of intensive data preprocessing procedures to tackle the problem of imbalanced classes and improve the performance of the constructed classifiers.These procedures involves applying data transformation, normalization, standardization, re-sampling and data reduction procedures using a number of variables importance scorers. The best performance was achieved by building an MLP model that was trained and tested using five-fold cross-validation using datasets that were re-sampled using SMOTE method and then reduced using SVM variable importance scorer. This model was successful in classifying samples with excellent accuracy and also in identifying the potential disease biomarkers. The results confirm the validity of metabolomics data mining for diagnosis of cachexia. It also emphasizes the importance of data preprocessing procedures such as sampling and data reduction for improving data mining results, particularly when data suffers from the problem of imbalanced classes.
{"title":"Enhancing Learning from Imbalanced Classes via Data Preprocessing: A Data-Driven Application in Metabolomics Data Mining","authors":"Ahmed Banimustafa","doi":"10.22042/ISECURE.2019.11.0.11","DOIUrl":"https://doi.org/10.22042/ISECURE.2019.11.0.11","url":null,"abstract":"This paper presents a data mining application in metabolomics. It aims at building an enhanced machine learning classifier that can be used for diagnosing cachexia syndrome and identifying its involved biomarkers. To achieve this goal, a data-driven analysis is carried out using a public dataset consisting of 1H-NMR metabolite profile. This dataset suffers from the problem of imbalanced classes which is known to deteriorate the performance of classifiers. It also influences its validity and generalizablity. The classification models in this study were built using five machine learning algorithms known as PLS-DA, MLP, SVM, C4.5 and ID3. This model is built after carrying out a number of intensive data preprocessing procedures to tackle the problem of imbalanced classes and improve the performance of the constructed classifiers.These procedures involves applying data transformation, normalization, standardization, re-sampling and data reduction procedures using a number of variables importance scorers. The best performance was achieved by building an MLP model that was trained and tested using five-fold cross-validation using datasets that were re-sampled using SMOTE method and then reduced using SVM variable importance scorer. This model was successful in classifying samples with excellent accuracy and also in identifying the potential disease biomarkers. The results confirm the validity of metabolomics data mining for diagnosis of cachexia. It also emphasizes the importance of data preprocessing procedures such as sampling and data reduction for improving data mining results, particularly when data suffers from the problem of imbalanced classes.","PeriodicalId":436674,"journal":{"name":"ISC Int. J. Inf. Secur.","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114255896","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 : 2019-08-01DOI: 10.22042/ISECURE.2019.11.0.13
M. Memon, Z. Hassan, K. Dahri, Asadullah Shaikh, M. A. Nizamani
With the emerging concept of model transformation, information can be extracted from one or more source models to produce the target models. The conversion of these models can be done automatically with specific transformation languages. This conversion requires mapping between both models with the help of dynamic hash tables. Hash tables store reference links between the elements of the source and target model. Whenever there is a need to access the target element, we query the hash table. In contrast, this paper presents an approach by directly creating aspects in the source meta-model with traces. These traces hold references to target elements during the execution. Illustrating the idea of model driven engineering (MDE), This paper proposes a method that transforms UML class models to EMF ECORE model.
{"title":"Aspect Oriented UML to ECORE Model Transformation","authors":"M. Memon, Z. Hassan, K. Dahri, Asadullah Shaikh, M. A. Nizamani","doi":"10.22042/ISECURE.2019.11.0.13","DOIUrl":"https://doi.org/10.22042/ISECURE.2019.11.0.13","url":null,"abstract":"With the emerging concept of model transformation, information can be extracted from one or more source models to produce the target models. The conversion of these models can be done automatically with specific transformation languages. This conversion requires mapping between both models with the help of dynamic hash tables. Hash tables store reference links between the elements of the source and target model. Whenever there is a need to access the target element, we query the hash table. In contrast, this paper presents an approach by directly creating aspects in the source meta-model with traces. These traces hold references to target elements during the execution. Illustrating the idea of model driven engineering (MDE), This paper proposes a method that transforms UML class models to EMF ECORE model.","PeriodicalId":436674,"journal":{"name":"ISC Int. J. Inf. Secur.","volume":"271 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124396463","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 : 2019-07-01DOI: 10.22042/ISECURE.2019.11.0.2
Hadjer Saadi, M. Yagoub, R. Touhami
The Internet of Things (IoT) is a very encouraging and fast-growing area that brings together the benefits of wireless systems, sensor networks, actuators, etc.A wide range of IoT applications have been targeted and several aspects of this field have been identified to address specific issues, as well as technologies and standards developed in various domains such as in radio frequency identification(RFID), sensors, and mobile telephony, to name a few. This article aims to talk specifically about the RFID technology and its accompanying communication, authentication, risk, and security concerns while applied to the IoT field. An important part of this work is indeed focused on security aspects that derive from the use of RFID in IoT, especially in IoT networks. The results of our research work highlighted an excellent integration of RFID in the field of Internet of things, particularly in healthcare systems.
{"title":"Role and Application of RFID Technology in Internet of Things: Communication, Authentication, Risk, and Security Concerns","authors":"Hadjer Saadi, M. Yagoub, R. Touhami","doi":"10.22042/ISECURE.2019.11.0.2","DOIUrl":"https://doi.org/10.22042/ISECURE.2019.11.0.2","url":null,"abstract":"The Internet of Things (IoT) is a very encouraging and fast-growing area that brings together the benefits of wireless systems, sensor networks, actuators, etc.A wide range of IoT applications have been targeted and several aspects of this field have been identified to address specific issues, as well as technologies and standards developed in various domains such as in radio frequency identification(RFID), sensors, and mobile telephony, to name a few. This article aims to talk specifically about the RFID technology and its accompanying communication, authentication, risk, and security concerns while applied to the IoT field. An important part of this work is indeed focused on security aspects that derive from the use of RFID in IoT, especially in IoT networks. The results of our research work highlighted an excellent integration of RFID in the field of Internet of things, particularly in healthcare systems.","PeriodicalId":436674,"journal":{"name":"ISC Int. J. Inf. Secur.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130620514","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 : 2019-07-01DOI: 10.22042/ISECURE.2019.11.0.4
Dalia Shaaban, M. Saad, A. Madian, H. Elmahdy
Medical images show a great interest since it is needed in various medical applications. In order to decrease the size of medical images which are needed to be transmitted in a faster way; Region of Interest (ROI) and hybrid lossless compression techniques are applied on medical images to be compressed without losing important data. In this paper, a proposed model will be presented and assessed based on size of the image, the Peak Signal to Noise Ratio (PSNR),and the time that is required to compress and reconstruct the original image.The major objective of the proposed model is to minimize the size of image and the transmission time. Moreover, improving the PSNR is a critical challenge.The results of the proposed model illustrate that applying hybrid losslesstechniques on the ROI of medical images reduces size by 39% and gives better results in terms of the compression ratio and PSNR.
{"title":"Medical Image Compression Based on Region of Interest","authors":"Dalia Shaaban, M. Saad, A. Madian, H. Elmahdy","doi":"10.22042/ISECURE.2019.11.0.4","DOIUrl":"https://doi.org/10.22042/ISECURE.2019.11.0.4","url":null,"abstract":"Medical images show a great interest since it is needed in various medical applications. In order to decrease the size of medical images which are needed to be transmitted in a faster way; Region of Interest (ROI) and hybrid lossless compression techniques are applied on medical images to be compressed without losing important data. In this paper, a proposed model will be presented and assessed based on size of the image, the Peak Signal to Noise Ratio (PSNR),and the time that is required to compress and reconstruct the original image.The major objective of the proposed model is to minimize the size of image and the transmission time. Moreover, improving the PSNR is a critical challenge.The results of the proposed model illustrate that applying hybrid losslesstechniques on the ROI of medical images reduces size by 39% and gives better results in terms of the compression ratio and PSNR.","PeriodicalId":436674,"journal":{"name":"ISC Int. J. Inf. Secur.","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125925096","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 : 2019-07-01DOI: 10.22042/ISECURE.2019.11.0.1
O. Letychevskyi, Y. Hryniuk, V. Yakovlev, V. Peschanenko, V. Radchenko
This paper explores the algebraic matching approach for detection of vulnerabilities in binary codes. The algebraic programming system is used for implementing this method. It is anticipated that models of vulnerabilities and programs to be verified are presented as behavior algebra and action language specifications. The methods of algebraic matching are based on rewriting rules and techniques with usage of conditional rewriting. This process is combined with symbolic modeling that gives a possibility to provide accurate detection of vulnerabilities. The paper provides examples of formalization of vulnerability models and translation of binary codes to behavior algebra expressions.
{"title":"Algebraic Matching of Vulnerabilities in a Low-Level Code","authors":"O. Letychevskyi, Y. Hryniuk, V. Yakovlev, V. Peschanenko, V. Radchenko","doi":"10.22042/ISECURE.2019.11.0.1","DOIUrl":"https://doi.org/10.22042/ISECURE.2019.11.0.1","url":null,"abstract":"This paper explores the algebraic matching approach for detection of vulnerabilities in binary codes. The algebraic programming system is used for implementing this method. It is anticipated that models of vulnerabilities and programs to be verified are presented as behavior algebra and action language specifications. The methods of algebraic matching are based on rewriting rules and techniques with usage of conditional rewriting. This process is combined with symbolic modeling that gives a possibility to provide accurate detection of vulnerabilities. The paper provides examples of formalization of vulnerability models and translation of binary codes to behavior algebra expressions.","PeriodicalId":436674,"journal":{"name":"ISC Int. J. Inf. Secur.","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125987744","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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