M. Gafsi, Rim Amdouni, Mohamed Ali Hajjaji, J. Malek, A. Mtibaa
This article puts forward a fast chaos‐RSA‐based hybrid cryptosystem to secure and authenticate secret images. The SHA‐512 is used to generate a 512‐bit initial key. The RSA system is used to encrypt the initial secret key and signature generation for both the sender and image authentication. In fact, a powerful block‐cipher algorithm is developed to encrypt and decrypt images with a high level of security. At this stage, a strong PRNG based on four chaotic systems is propounded to generate high‐quality keys. Therefore, an improved architecture is suggested. It performs confusion and diffusion of images with low computational complexity. In the final step, the encrypted secret key, signature, and encrypted image are combined together in order to obtain an encrypted signed image. The block‐cipher algorithm is evaluated in‐depth for several ordinary and medical images with different types, content, and size. The obtained simulation results demonstrate that the system enables high‐level security. The entropy has achieved a value of 7.9998 which is the most important feature of randomness. A comparative study against numerous recent encryption algorithms demonstrates that the proposed algorithm provides good results.
{"title":"Improved chaos‐RSA‐based hybrid cryptosystem for image encryption and authentication","authors":"M. Gafsi, Rim Amdouni, Mohamed Ali Hajjaji, J. Malek, A. Mtibaa","doi":"10.1002/cpe.7187","DOIUrl":"https://doi.org/10.1002/cpe.7187","url":null,"abstract":"This article puts forward a fast chaos‐RSA‐based hybrid cryptosystem to secure and authenticate secret images. The SHA‐512 is used to generate a 512‐bit initial key. The RSA system is used to encrypt the initial secret key and signature generation for both the sender and image authentication. In fact, a powerful block‐cipher algorithm is developed to encrypt and decrypt images with a high level of security. At this stage, a strong PRNG based on four chaotic systems is propounded to generate high‐quality keys. Therefore, an improved architecture is suggested. It performs confusion and diffusion of images with low computational complexity. In the final step, the encrypted secret key, signature, and encrypted image are combined together in order to obtain an encrypted signed image. The block‐cipher algorithm is evaluated in‐depth for several ordinary and medical images with different types, content, and size. The obtained simulation results demonstrate that the system enables high‐level security. The entropy has achieved a value of 7.9998 which is the most important feature of randomness. A comparative study against numerous recent encryption algorithms demonstrates that the proposed algorithm provides good results.","PeriodicalId":10584,"journal":{"name":"Concurrency and Computation: Practice and Experience","volume":"42 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89035601","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}
Internet of things (IoT) has evolved exponentially in the recent years and its applications are also being explored in the medical field. Due to this, the volume of medical images transmitted has increased multifold. Usage of IoT networks for medical image transmission has significantly reduced the time needed for clinical diagnosis and thereby increasing treatment efficiency. However at present, IoT networks are open to various security threats, which may affect the sensitive and private information that are present in patient's medical image datasets. Existing studies reveal the need of improvisation for secured medical data transmission over IoT networks. In the context to IoT security issues, this research paper proposes blockchain architecture integrated with chaotic encrypted medical image transmission to ensure the high security in medical image transmission. The proposed system incorporates tri‐layered architecture such as Image Aware Segmentation (IAS), hybrid chaotic encryption scheme and finally blockchain environment. The extensive experimentation has been carried out in which the performance parameters such as entropy, NACI and UACI (Number of Pixel Change Ratio and Unified Average Changed Intensity) were calculated and analyzed. It is found that the proposed architecture has NPCR as 99.65%, UACI as 33.95% and entropy ideally close to 8. Encryption results show that the proposed architecture exhibited more randomness, which can defend the IoT security threats.
{"title":"B‐SCORE – A blockchain based hybrid chaotic signatures for medical image encryption in an IoT environment","authors":"N. R, Ponsy R. K. Sathia Bhama","doi":"10.1002/cpe.7115","DOIUrl":"https://doi.org/10.1002/cpe.7115","url":null,"abstract":"Internet of things (IoT) has evolved exponentially in the recent years and its applications are also being explored in the medical field. Due to this, the volume of medical images transmitted has increased multifold. Usage of IoT networks for medical image transmission has significantly reduced the time needed for clinical diagnosis and thereby increasing treatment efficiency. However at present, IoT networks are open to various security threats, which may affect the sensitive and private information that are present in patient's medical image datasets. Existing studies reveal the need of improvisation for secured medical data transmission over IoT networks. In the context to IoT security issues, this research paper proposes blockchain architecture integrated with chaotic encrypted medical image transmission to ensure the high security in medical image transmission. The proposed system incorporates tri‐layered architecture such as Image Aware Segmentation (IAS), hybrid chaotic encryption scheme and finally blockchain environment. The extensive experimentation has been carried out in which the performance parameters such as entropy, NACI and UACI (Number of Pixel Change Ratio and Unified Average Changed Intensity) were calculated and analyzed. It is found that the proposed architecture has NPCR as 99.65%, UACI as 33.95% and entropy ideally close to 8. Encryption results show that the proposed architecture exhibited more randomness, which can defend the IoT security threats.","PeriodicalId":10584,"journal":{"name":"Concurrency and Computation: Practice and Experience","volume":"50 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91134093","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In recent years, the WSN are emerging swiftly since it finds applications in various domains including weather monitoring, attack detection, industrial monitoring, monitoring of submarine organisms, patient monitoring as well as the monitoring of ecological disorders. But WSN is also influenced by various other factors like network lifetime and energy consumption. It is necessary to provide an energy effective protocol to conquer certain troubles that includes packet delivery ratio, network lifetime, residual energy as well as effective routing in WSN. Therefore, this article aims to propose a novel protocol to enhance the energy efficiency of the network thereby providing an optimal routing path. This can be achieved by selecting an optimal cluster head that maintains communication between the base station and the sensor node. In this article, a novel multi‐objective moth swarm based sailfish (MOMS‐SF) technique is employed in selecting an optimal cluster head. The proposed MOMS‐SF technique enhances the network lifetime and minimizes the energy consumption of the network. Finally, the evaluation results are conducted to determine the network performances of the proposed MOMS‐SF approach. Also, a comparative analysis is carried out and the graphical analyzes for various parameters are made for various approaches to determine the effectiveness of the proposed system.
{"title":"Multi‐objective moth swarm based sailfish model for optimal routing in wireless sensor network","authors":"M. Gunasekar, Gobalakrishnan Natesan, D. Samiayya","doi":"10.1002/cpe.7125","DOIUrl":"https://doi.org/10.1002/cpe.7125","url":null,"abstract":"In recent years, the WSN are emerging swiftly since it finds applications in various domains including weather monitoring, attack detection, industrial monitoring, monitoring of submarine organisms, patient monitoring as well as the monitoring of ecological disorders. But WSN is also influenced by various other factors like network lifetime and energy consumption. It is necessary to provide an energy effective protocol to conquer certain troubles that includes packet delivery ratio, network lifetime, residual energy as well as effective routing in WSN. Therefore, this article aims to propose a novel protocol to enhance the energy efficiency of the network thereby providing an optimal routing path. This can be achieved by selecting an optimal cluster head that maintains communication between the base station and the sensor node. In this article, a novel multi‐objective moth swarm based sailfish (MOMS‐SF) technique is employed in selecting an optimal cluster head. The proposed MOMS‐SF technique enhances the network lifetime and minimizes the energy consumption of the network. Finally, the evaluation results are conducted to determine the network performances of the proposed MOMS‐SF approach. Also, a comparative analysis is carried out and the graphical analyzes for various parameters are made for various approaches to determine the effectiveness of the proposed system.","PeriodicalId":10584,"journal":{"name":"Concurrency and Computation: Practice and Experience","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88219804","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In order to improve the availability and persistence of data, lightweight cloud users want to store multiple‐replicas of the original file on the server with less local computing and storage overhead. Meanwhile, to ensure the integrity of the remote storage data, some schemes have been designed to allow public verification. However, most existing schemes only focus on malicious cloud service providers and ignore the possibility that dishonest users cheat for profit. This article implements an arbitrable data auditing scheme under multi‐replica storage. The scheme adopts a new arbitration mechanism under multi‐replica storage, makes use of the non‐tampering characteristics of smart contracts, carries out reliable verification through miners, and realizes the timely detection and punishment of any fraudulent entity. In addition, the scheme also designs a multi‐replica storage model based on the B* tree, realizes the batch verification of replica blocks, enables the fraud behavior of malicious users to be identified after data update, and improves the space utilization efficiency. The article also gives detailed security proof of the proposed scheme. The evaluation result shows our scheme not only realizes a more practical and fairer audit scheme but also has lower computational overhead than current state‐of‐the‐art multi‐replica arbitrable schemes.
{"title":"An arbitrable multi‐replica data auditing scheme based on smart contracts","authors":"Junfeng Tian, Qian Yang","doi":"10.1002/cpe.7164","DOIUrl":"https://doi.org/10.1002/cpe.7164","url":null,"abstract":"In order to improve the availability and persistence of data, lightweight cloud users want to store multiple‐replicas of the original file on the server with less local computing and storage overhead. Meanwhile, to ensure the integrity of the remote storage data, some schemes have been designed to allow public verification. However, most existing schemes only focus on malicious cloud service providers and ignore the possibility that dishonest users cheat for profit. This article implements an arbitrable data auditing scheme under multi‐replica storage. The scheme adopts a new arbitration mechanism under multi‐replica storage, makes use of the non‐tampering characteristics of smart contracts, carries out reliable verification through miners, and realizes the timely detection and punishment of any fraudulent entity. In addition, the scheme also designs a multi‐replica storage model based on the B* tree, realizes the batch verification of replica blocks, enables the fraud behavior of malicious users to be identified after data update, and improves the space utilization efficiency. The article also gives detailed security proof of the proposed scheme. The evaluation result shows our scheme not only realizes a more practical and fairer audit scheme but also has lower computational overhead than current state‐of‐the‐art multi‐replica arbitrable schemes.","PeriodicalId":10584,"journal":{"name":"Concurrency and Computation: Practice and Experience","volume":"18 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88450474","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}
N. Sirisala, Anitha Yarava, Y. P. Reddy, Veeresh Poola
Social network (OSN) is an emerging platform through which people can connect with their friends, relatives, and other like‐minded people. On the other hand, users' personal information might be misused because of other users' biased and malicious behavior. Establishing a trusted environment in social networks is one of the current research problems. Some of the research papers proposed to trust computational methods, but still, there is a lack of methods to handle biased recommendations and loss of trust accuracy towards the target user. In this article, to address these open issues, “a novel trust recommendation model in online social networks using soft computing methods (TRMSC)” is proposed for the Twitter social networks. Here direct and indirect trust is computed for known and unknown users, respectively. The direct trust of a user is computed using clustering methods based on his social activities (posts, retweets received, mentions received, listed count, and follower count) with other users. In the computation of indirect trust, the impact of biased recommendations is suppressed using the Dempster Shafer theory(DST) method, and loss of trust is minimized using trust transitive matrices. The performance of the proposed method is analyzed theoretically and experimentally. Time and space complexities are measured using asymptotic notations. In experimental results, TRMSC is evaluated for different network sizes and for target users at different distances (2 to 4‐hops), where it could perform better than existing methods.
{"title":"A novel trust recommendation model in online social networks using soft computing methods","authors":"N. Sirisala, Anitha Yarava, Y. P. Reddy, Veeresh Poola","doi":"10.1002/cpe.7153","DOIUrl":"https://doi.org/10.1002/cpe.7153","url":null,"abstract":"Social network (OSN) is an emerging platform through which people can connect with their friends, relatives, and other like‐minded people. On the other hand, users' personal information might be misused because of other users' biased and malicious behavior. Establishing a trusted environment in social networks is one of the current research problems. Some of the research papers proposed to trust computational methods, but still, there is a lack of methods to handle biased recommendations and loss of trust accuracy towards the target user. In this article, to address these open issues, “a novel trust recommendation model in online social networks using soft computing methods (TRMSC)” is proposed for the Twitter social networks. Here direct and indirect trust is computed for known and unknown users, respectively. The direct trust of a user is computed using clustering methods based on his social activities (posts, retweets received, mentions received, listed count, and follower count) with other users. In the computation of indirect trust, the impact of biased recommendations is suppressed using the Dempster Shafer theory(DST) method, and loss of trust is minimized using trust transitive matrices. The performance of the proposed method is analyzed theoretically and experimentally. Time and space complexities are measured using asymptotic notations. In experimental results, TRMSC is evaluated for different network sizes and for target users at different distances (2 to 4‐hops), where it could perform better than existing methods.","PeriodicalId":10584,"journal":{"name":"Concurrency and Computation: Practice and Experience","volume":"41 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76319845","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study attempts to investigate the cross‐correlation between stocks listed under the XU100 index of Borsa Istanbul with several ratios and indices of the stock markets worldwide by using the Random Matrix Theory approach through a correlation matrix. In addition, Eigenvector Analysis, Network Analysis, Dimension Reduction will be carried out to investigate cross‐correlation between markets. It was found that XU100, which is an index that includes 100 stocks highest in volume, has a distinguishing behavior compared to other indices and rates in terms of eigenvalue and related eigenvector structures. Furthermore, mean‐value portfolio analysis showed that the empirical correlation matrix underestimates the portfolio risks than the correlation matrix obtained by filtering the noise. Coronavirus pandemic also affected Borsa Istanbul by breaking periodic behavior of volatility and correlation cycle.
{"title":"The analysis of cross‐correlation between Istanbul Stock Exchange and major stock markets and indices: An empirical analysis using Random Matrix Theory","authors":"B. Tastan, Hatice Imamoglu","doi":"10.1002/cpe.7113","DOIUrl":"https://doi.org/10.1002/cpe.7113","url":null,"abstract":"This study attempts to investigate the cross‐correlation between stocks listed under the XU100 index of Borsa Istanbul with several ratios and indices of the stock markets worldwide by using the Random Matrix Theory approach through a correlation matrix. In addition, Eigenvector Analysis, Network Analysis, Dimension Reduction will be carried out to investigate cross‐correlation between markets. It was found that XU100, which is an index that includes 100 stocks highest in volume, has a distinguishing behavior compared to other indices and rates in terms of eigenvalue and related eigenvector structures. Furthermore, mean‐value portfolio analysis showed that the empirical correlation matrix underestimates the portfolio risks than the correlation matrix obtained by filtering the noise. Coronavirus pandemic also affected Borsa Istanbul by breaking periodic behavior of volatility and correlation cycle.","PeriodicalId":10584,"journal":{"name":"Concurrency and Computation: Practice and Experience","volume":"41 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73728155","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}
Tomato is a widely consumed fruit across the world due to its high nutritional values. Leaf diseases in tomato are very common which incurs huge damages but early detection of leaf diseases can help in avoiding that. The existing practices for detecting different diseases by the human experts are costly, time consuming and subjective in nature. Computer vision plays important role toward early detection of tomato leaf detection. However, implementation of computationally less expensive model and improvement of detection performance is still open. This article reports a computer vision based system to classify seven different categories of diseases, namely, bacterial spot, early blight, late blight, leaf mold, septoria leaf spot, spider mites, and target spots using optimized MobileNetV2 architecture. A modified gray wolf optimization approach has been adopted for optimization of MobileNetV2 hyperparameters for improved performance. The model has been validated using standard internal and external validation methods and found to provide the classification accuracy in the tune of 98%. The results reflect the promising potential of the presented framework for early detection of tomato leaf diseases which can help to avoid substantial agricultural loss.
{"title":"Identification of the types of disease for tomato plants using a modified gray wolf optimization optimized MobileNetV2 convolutional neural network architecture driven computer vision framework","authors":"G. Mukherjee, Arpitam Chatterjee, B. Tudu","doi":"10.1002/cpe.7161","DOIUrl":"https://doi.org/10.1002/cpe.7161","url":null,"abstract":"Tomato is a widely consumed fruit across the world due to its high nutritional values. Leaf diseases in tomato are very common which incurs huge damages but early detection of leaf diseases can help in avoiding that. The existing practices for detecting different diseases by the human experts are costly, time consuming and subjective in nature. Computer vision plays important role toward early detection of tomato leaf detection. However, implementation of computationally less expensive model and improvement of detection performance is still open. This article reports a computer vision based system to classify seven different categories of diseases, namely, bacterial spot, early blight, late blight, leaf mold, septoria leaf spot, spider mites, and target spots using optimized MobileNetV2 architecture. A modified gray wolf optimization approach has been adopted for optimization of MobileNetV2 hyperparameters for improved performance. The model has been validated using standard internal and external validation methods and found to provide the classification accuracy in the tune of 98%. The results reflect the promising potential of the presented framework for early detection of tomato leaf diseases which can help to avoid substantial agricultural loss.","PeriodicalId":10584,"journal":{"name":"Concurrency and Computation: Practice and Experience","volume":"22 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72914402","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}
Jiashu Wu, Hao Dai, Yang Wang, Shigen Shen, Chengzhong Xu
With the fast growing quantity of data generated by smart devices and the exponential surge of processing demand in the Internet of Things (IoT) era, the resource‐rich cloud centers have been utilized to tackle these challenges. To relieve the burden on cloud centers, edge‐cloud computation offloading becomes a promising solution since shortening the proximity between the data source and the computation by offloading computation tasks from the cloud to edge devices can improve performance and quality of service. Several optimization models of edge‐cloud computation offloading have been proposed that take computation costs and heterogeneous communication costs into account. However, several important factors are not jointly considered, such as heterogeneities of tasks, load balancing among nodes and the profit yielded by computation tasks, which lead to the profit and cost‐oriented computation offloading optimization model PECCO proposed in this article. Considering that the model is hard in nature and the optimization objective is not differentiable, we propose an improved Moth‐flame optimizer PECCO‐MFI which addresses some deficiencies of the original Moth‐flame optimizer and integrate it under the edge‐cloud environment. Comprehensive experiments are conducted to verify the superior performance of the proposed method when optimizing the proposed task offloading model under the edge‐cloud environment.
{"title":"PECCO: A profit and cost‐oriented computation offloading scheme in edge‐cloud environment with improved Moth‐flame optimization","authors":"Jiashu Wu, Hao Dai, Yang Wang, Shigen Shen, Chengzhong Xu","doi":"10.1002/cpe.7163","DOIUrl":"https://doi.org/10.1002/cpe.7163","url":null,"abstract":"With the fast growing quantity of data generated by smart devices and the exponential surge of processing demand in the Internet of Things (IoT) era, the resource‐rich cloud centers have been utilized to tackle these challenges. To relieve the burden on cloud centers, edge‐cloud computation offloading becomes a promising solution since shortening the proximity between the data source and the computation by offloading computation tasks from the cloud to edge devices can improve performance and quality of service. Several optimization models of edge‐cloud computation offloading have been proposed that take computation costs and heterogeneous communication costs into account. However, several important factors are not jointly considered, such as heterogeneities of tasks, load balancing among nodes and the profit yielded by computation tasks, which lead to the profit and cost‐oriented computation offloading optimization model PECCO proposed in this article. Considering that the model is hard in nature and the optimization objective is not differentiable, we propose an improved Moth‐flame optimizer PECCO‐MFI which addresses some deficiencies of the original Moth‐flame optimizer and integrate it under the edge‐cloud environment. Comprehensive experiments are conducted to verify the superior performance of the proposed method when optimizing the proposed task offloading model under the edge‐cloud environment.","PeriodicalId":10584,"journal":{"name":"Concurrency and Computation: Practice and Experience","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74528232","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}
A novel model of data similarity estimation and clustering method is proposed in this article to retrieve the relevant data with the best matching in big data processing. An advanced model of graph distance pattern (GDP) method with lexical subgroup (LS) system is used to estimate the similarity between the query data and the entire database. With the help of neural network, the relevancy of feature attributes in the database are predicted and matching index is sorted to provide the recommended data for given query data. This was achieved by using the correlated sim‐neural network (CSNN). This is an enhanced model of neural network technology to find the relevancy based on the correlation factor of feature set. The training process of CSNN classifier is carried by estimating the correlation factor of the attributes of dataset. These are forms as the clusters and paged with proper indexing based on the LS parameter of similarity metric. The results obtained by the proposed system for recall, precision, accuracy, error rate, F‐measure, kappa coefficient, specificity, and MCC are 0.98, 0.98, 0.97, 0.03, 0.99, 0.991, 0.986, and 0.984, respectively.
{"title":"An efficient framework for the similarity prediction with query recommendation in E‐learning system","authors":"Vedavathi Nagendra Prasad, Anil Kumar Kureekatil Muthappa","doi":"10.1002/cpe.7145","DOIUrl":"https://doi.org/10.1002/cpe.7145","url":null,"abstract":"A novel model of data similarity estimation and clustering method is proposed in this article to retrieve the relevant data with the best matching in big data processing. An advanced model of graph distance pattern (GDP) method with lexical subgroup (LS) system is used to estimate the similarity between the query data and the entire database. With the help of neural network, the relevancy of feature attributes in the database are predicted and matching index is sorted to provide the recommended data for given query data. This was achieved by using the correlated sim‐neural network (CSNN). This is an enhanced model of neural network technology to find the relevancy based on the correlation factor of feature set. The training process of CSNN classifier is carried by estimating the correlation factor of the attributes of dataset. These are forms as the clusters and paged with proper indexing based on the LS parameter of similarity metric. The results obtained by the proposed system for recall, precision, accuracy, error rate, F‐measure, kappa coefficient, specificity, and MCC are 0.98, 0.98, 0.97, 0.03, 0.99, 0.991, 0.986, and 0.984, respectively.","PeriodicalId":10584,"journal":{"name":"Concurrency and Computation: Practice and Experience","volume":"17 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89036034","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}
Yanhua Liu, Jiaqi Li, Baoxu Liu, Xiaoling Gao, Ximeng Liu
Malware detection is indispensable to cybersecurity. However, with the advent of new malware variants and scenarios with few and imbalanced samples, malware detection for various complex scenarios has been a very challenging problem. In this article, we propose a malware detection method based on image analysis and generative adversarial networks, named MadInG, which can improve the accuracy of malware detection for insufficient samples, sample imbalance, and new variants scenarios. Specifically, we first generate fixed‐size grayscale images of malware to reduce the workload of feature engineering or the involvement of domain expert knowledge on malware detection. Then we introduce auxiliary classifier generative adversarial networks into malware detection to enhance the generalization ability of the detector. Finally, we construct a variety of malware scenarios and compare our proposed method with existing popular detection methods. Extensive experimental results demonstrate that our method achieves high accuracy and well balance in malware detection for different scenarios, especially, the detection rate of malware variants reaches 99.5%.
{"title":"Malware detection method based on image analysis and generative adversarial networks","authors":"Yanhua Liu, Jiaqi Li, Baoxu Liu, Xiaoling Gao, Ximeng Liu","doi":"10.1002/cpe.7170","DOIUrl":"https://doi.org/10.1002/cpe.7170","url":null,"abstract":"Malware detection is indispensable to cybersecurity. However, with the advent of new malware variants and scenarios with few and imbalanced samples, malware detection for various complex scenarios has been a very challenging problem. In this article, we propose a malware detection method based on image analysis and generative adversarial networks, named MadInG, which can improve the accuracy of malware detection for insufficient samples, sample imbalance, and new variants scenarios. Specifically, we first generate fixed‐size grayscale images of malware to reduce the workload of feature engineering or the involvement of domain expert knowledge on malware detection. Then we introduce auxiliary classifier generative adversarial networks into malware detection to enhance the generalization ability of the detector. Finally, we construct a variety of malware scenarios and compare our proposed method with existing popular detection methods. Extensive experimental results demonstrate that our method achieves high accuracy and well balance in malware detection for different scenarios, especially, the detection rate of malware variants reaches 99.5%.","PeriodicalId":10584,"journal":{"name":"Concurrency and Computation: Practice and Experience","volume":"42 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77254322","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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