Pub Date : 2023-12-13DOI: 10.1186/s13635-023-00149-w
Ming Li
Wireless sensor networks, as an emerging information exchange technology, have been widely applied in many fields. However, nodes tend to become damaged in harsh and complex environmental conditions. In order to effectively diagnose node faults, a Bayesian model-based node fault diagnosis model was proposed. Firstly, a comprehensive analysis was conducted into the operative principles of wireless sensor systems, whereby fault-related features were then extrapolated. A Bayesian diagnostic model was constructed using the maximum likelihood method with sufficient sample features, and a joint tree model was introduced for node diagnosis. Due to the insufficient accuracy of Bayesian models in processing small sample data, a constrained maximum entropy method was proposed as the prediction module of the model. The use of small sample data to obtain the initial model parameters leads to improved performance and accuracy of the model. During parameter learning tests, the limited maximum entropy model outperformed the other two learning models on a smaller dataset of 35 with a distance value of 2.65. In node fault diagnosis, the diagnostic time of the three models was compared, and the average diagnostic time of the proposed diagnostic model was 41.2 seconds. In the node diagnosis accuracy test, the proposed model has the highest node fault diagnosis accuracy, with an average diagnosis accuracy of 0.946, which is superior to the other two models. In summary, the node fault diagnosis model based on Bayesian model proposed in this study has important research significance and practical application value in wireless sensor networks. By improving the reliability and maintenance efficiency of the network, this model provides strong support for the development and application of wireless sensor networks.
{"title":"Node fault diagnosis algorithm for wireless sensor networks based on BN and WSN","authors":"Ming Li","doi":"10.1186/s13635-023-00149-w","DOIUrl":"https://doi.org/10.1186/s13635-023-00149-w","url":null,"abstract":"Wireless sensor networks, as an emerging information exchange technology, have been widely applied in many fields. However, nodes tend to become damaged in harsh and complex environmental conditions. In order to effectively diagnose node faults, a Bayesian model-based node fault diagnosis model was proposed. Firstly, a comprehensive analysis was conducted into the operative principles of wireless sensor systems, whereby fault-related features were then extrapolated. A Bayesian diagnostic model was constructed using the maximum likelihood method with sufficient sample features, and a joint tree model was introduced for node diagnosis. Due to the insufficient accuracy of Bayesian models in processing small sample data, a constrained maximum entropy method was proposed as the prediction module of the model. The use of small sample data to obtain the initial model parameters leads to improved performance and accuracy of the model. During parameter learning tests, the limited maximum entropy model outperformed the other two learning models on a smaller dataset of 35 with a distance value of 2.65. In node fault diagnosis, the diagnostic time of the three models was compared, and the average diagnostic time of the proposed diagnostic model was 41.2 seconds. In the node diagnosis accuracy test, the proposed model has the highest node fault diagnosis accuracy, with an average diagnosis accuracy of 0.946, which is superior to the other two models. In summary, the node fault diagnosis model based on Bayesian model proposed in this study has important research significance and practical application value in wireless sensor networks. By improving the reliability and maintenance efficiency of the network, this model provides strong support for the development and application of wireless sensor networks.","PeriodicalId":46070,"journal":{"name":"EURASIP Journal on Information Security","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2023-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138630473","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 : 2023-12-11DOI: 10.1186/s13635-023-00148-x
Jimin Zhang, Xianfeng Zhao, Xiaolei He
Because the JPEG recompression in social networks changes the DCT coefficients of uploaded images, applying image steganography in popular image-sharing social networks requires robustness. Currently, most robust steganography algorithms rely on the resistance of embedding to the general JPEG recompression process. The operations in a specific compression channel are usually ignored, which reduces the robustness performance. Besides, to acquire the robust cover image, the state-of-the-art robust steganography needs to upload the cover image to social networks several times, which may be insecure regarding behavior security. In this paper, a robust steganography method based on the softmax outputs of a trained classifier and protocol message embedding is proposed. In the proposed method, a deep learning-based robustness classifier is trained to model the specific process of the JPEG recompression channel. The prediction result of the classifier is used to select the robust DCT blocks to form the embedding domain. The selection information is embedded as the protocol messages into the middle-frequency coefficients of DCT blocks. To further improve the recovery possibility of the protocol message, a robustness enhancement method is proposed. It decreases the predicted non-robust possibility of the robustness classifier by modifying low-frequency coefficients of DCT blocks. The experimental results show that the proposed method has better robustness performance compared with state-of-the-art robust steganography and does not have the disadvantage regarding behavior security. The method is universal and can be implemented in different JPEG compression channels after fine-tuning the classifier. Moreover, it has better security performance compared with the state-of-the-art method when embedding large-sized secret messages.
{"title":"Robust JPEG steganography based on the robustness classifier","authors":"Jimin Zhang, Xianfeng Zhao, Xiaolei He","doi":"10.1186/s13635-023-00148-x","DOIUrl":"https://doi.org/10.1186/s13635-023-00148-x","url":null,"abstract":"Because the JPEG recompression in social networks changes the DCT coefficients of uploaded images, applying image steganography in popular image-sharing social networks requires robustness. Currently, most robust steganography algorithms rely on the resistance of embedding to the general JPEG recompression process. The operations in a specific compression channel are usually ignored, which reduces the robustness performance. Besides, to acquire the robust cover image, the state-of-the-art robust steganography needs to upload the cover image to social networks several times, which may be insecure regarding behavior security. In this paper, a robust steganography method based on the softmax outputs of a trained classifier and protocol message embedding is proposed. In the proposed method, a deep learning-based robustness classifier is trained to model the specific process of the JPEG recompression channel. The prediction result of the classifier is used to select the robust DCT blocks to form the embedding domain. The selection information is embedded as the protocol messages into the middle-frequency coefficients of DCT blocks. To further improve the recovery possibility of the protocol message, a robustness enhancement method is proposed. It decreases the predicted non-robust possibility of the robustness classifier by modifying low-frequency coefficients of DCT blocks. The experimental results show that the proposed method has better robustness performance compared with state-of-the-art robust steganography and does not have the disadvantage regarding behavior security. The method is universal and can be implemented in different JPEG compression channels after fine-tuning the classifier. Moreover, it has better security performance compared with the state-of-the-art method when embedding large-sized secret messages.","PeriodicalId":46070,"journal":{"name":"EURASIP Journal on Information Security","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2023-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138566733","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 : 2023-11-23DOI: 10.1186/s13635-023-00146-z
Shaojun Chen
With the growth of the Internet, network security issues have become increasingly complex, and the importance of node interaction security is also gradually becoming prominent. At present, research on network security protection mainly starts from the overall perspective, and some studies also start from the interaction between nodes. However, the trust management mechanisms in these studies do not have a predictive function. Therefore, to predict trust levels and protect network security, this paper innovatively proposes a trust management system for network security protection based on the improved hidden Markov model. The research divides the trust level of inter-node interactions by calculating the threat level of inter-node interactions and predicts the trust level of inter-node interactions through an optimized hidden Markov model. In addition, the study designs an estimation of the types of interactive threats between nodes based on alarm data. The research results show that when inactive interaction tuples are not excluded, the average prediction accuracy of the combined model is 95.5%. In response time, the maximum values of the active and passive cluster management pages are 38 ms and 33 ms, respectively, while the minimum values are 16 ms and 14 ms, with an average of 26.2 ms and 24 ms, respectively. The trust management system designed by the research institute has good performance and can provide systematic support for network security protection, which has good practical significance.
{"title":"The design of network security protection trust management system based on an improved hidden Markov model","authors":"Shaojun Chen","doi":"10.1186/s13635-023-00146-z","DOIUrl":"https://doi.org/10.1186/s13635-023-00146-z","url":null,"abstract":"With the growth of the Internet, network security issues have become increasingly complex, and the importance of node interaction security is also gradually becoming prominent. At present, research on network security protection mainly starts from the overall perspective, and some studies also start from the interaction between nodes. However, the trust management mechanisms in these studies do not have a predictive function. Therefore, to predict trust levels and protect network security, this paper innovatively proposes a trust management system for network security protection based on the improved hidden Markov model. The research divides the trust level of inter-node interactions by calculating the threat level of inter-node interactions and predicts the trust level of inter-node interactions through an optimized hidden Markov model. In addition, the study designs an estimation of the types of interactive threats between nodes based on alarm data. The research results show that when inactive interaction tuples are not excluded, the average prediction accuracy of the combined model is 95.5%. In response time, the maximum values of the active and passive cluster management pages are 38 ms and 33 ms, respectively, while the minimum values are 16 ms and 14 ms, with an average of 26.2 ms and 24 ms, respectively. The trust management system designed by the research institute has good performance and can provide systematic support for network security protection, which has good practical significance.","PeriodicalId":46070,"journal":{"name":"EURASIP Journal on Information Security","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2023-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138506654","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 : 2023-10-19DOI: 10.1186/s13635-023-00144-1
Dan Wang, Qing Wu, Ming Hu
Abstract Currently, sensor energy assembly in wireless sensor networks is limited, and clustering methods are not effective to improve sensor energy consumption rate. Thus, a hierarchical energy-saving routing algorithm based on fuzzy logic was constructed by considering three aspects: residual energy value, centrality, and distance value between nodes and base stations. The remaining sensor nodes selected by fuzzy logic algorithm have a longer time to live and greater residual energy than those selected by low-power adaptive clustering hierarchical protocol algorithm, fuzzy unequal clustering algorithm, and fuzzy logic cluster head election algorithm. For network life cycle, the number of rounds in which the first dead node appears, in descending order, is studied: energy-saving routing algorithm (400 rounds) > new geographic cellular structure algorithm (300 rounds) > virtual grid based dynamic routes adjustment algorithm (100 rounds). Under the same experimental round, energy-saving routing algorithm’s remaining energy curve always reaches its maximum. The energy-saving routing algorithm by fuzzy logic constructed by this research institute can significantly improve network energy utilization, which has certain reference value.
{"title":"Hierarchical energy-saving routing algorithm using fuzzy logic in wireless sensor networks","authors":"Dan Wang, Qing Wu, Ming Hu","doi":"10.1186/s13635-023-00144-1","DOIUrl":"https://doi.org/10.1186/s13635-023-00144-1","url":null,"abstract":"Abstract Currently, sensor energy assembly in wireless sensor networks is limited, and clustering methods are not effective to improve sensor energy consumption rate. Thus, a hierarchical energy-saving routing algorithm based on fuzzy logic was constructed by considering three aspects: residual energy value, centrality, and distance value between nodes and base stations. The remaining sensor nodes selected by fuzzy logic algorithm have a longer time to live and greater residual energy than those selected by low-power adaptive clustering hierarchical protocol algorithm, fuzzy unequal clustering algorithm, and fuzzy logic cluster head election algorithm. For network life cycle, the number of rounds in which the first dead node appears, in descending order, is studied: energy-saving routing algorithm (400 rounds) > new geographic cellular structure algorithm (300 rounds) > virtual grid based dynamic routes adjustment algorithm (100 rounds). Under the same experimental round, energy-saving routing algorithm’s remaining energy curve always reaches its maximum. The energy-saving routing algorithm by fuzzy logic constructed by this research institute can significantly improve network energy utilization, which has certain reference value.","PeriodicalId":46070,"journal":{"name":"EURASIP Journal on Information Security","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135778295","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 : 2023-10-03DOI: 10.1186/s13635-023-00143-2
Olga Taran, Joakim Tutt, Taras Holotyak, Roman Chaban, Slavi Bonev, Slava Voloshynovskiy
{"title":"Correction: Mobile authentication of copy detection patterns","authors":"Olga Taran, Joakim Tutt, Taras Holotyak, Roman Chaban, Slavi Bonev, Slava Voloshynovskiy","doi":"10.1186/s13635-023-00143-2","DOIUrl":"https://doi.org/10.1186/s13635-023-00143-2","url":null,"abstract":"","PeriodicalId":46070,"journal":{"name":"EURASIP Journal on Information Security","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135739074","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}
{"title":"User authentication and access control to blockchain-based forensic log data","authors":"Md. Ezazul Islam, Md. Rafiqul Islam, Madhu Chetty, Suryani Lim, Mehmood A. Chadhar","doi":"10.1186/s13635-023-00142-3","DOIUrl":"https://doi.org/10.1186/s13635-023-00142-3","url":null,"abstract":"","PeriodicalId":46070,"journal":{"name":"EURASIP Journal on Information Security","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2023-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45851146","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 : 2023-06-26DOI: 10.1186/s13635-023-00139-y
Xiaoning Wang, Jia Liu, Chunjiong Zhang
{"title":"Network intrusion detection based on multi-domain data and ensemble-bidirectional LSTM","authors":"Xiaoning Wang, Jia Liu, Chunjiong Zhang","doi":"10.1186/s13635-023-00139-y","DOIUrl":"https://doi.org/10.1186/s13635-023-00139-y","url":null,"abstract":"","PeriodicalId":46070,"journal":{"name":"EURASIP Journal on Information Security","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2023-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46475057","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 : 2023-03-10DOI: 10.1186/s13635-023-00137-0
Arslan Ali, Andrea Migliorati, T. Bianchi, E. Magli
{"title":"Gaussian class-conditional simplex loss for accurate, adversarially robust deep classifier training","authors":"Arslan Ali, Andrea Migliorati, T. Bianchi, E. Magli","doi":"10.1186/s13635-023-00137-0","DOIUrl":"https://doi.org/10.1186/s13635-023-00137-0","url":null,"abstract":"","PeriodicalId":46070,"journal":{"name":"EURASIP Journal on Information Security","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2023-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48925705","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 : 2023-03-08DOI: 10.1186/s13635-023-00138-z
Jen-Ho Yang
{"title":"A multi-gateway authentication and key-agreement scheme on wireless sensor networks for IoT","authors":"Jen-Ho Yang","doi":"10.1186/s13635-023-00138-z","DOIUrl":"https://doi.org/10.1186/s13635-023-00138-z","url":null,"abstract":"","PeriodicalId":46070,"journal":{"name":"EURASIP Journal on Information Security","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2023-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44646077","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 : 2023-01-01Epub Date: 2023-06-06DOI: 10.1186/s13635-023-00140-5
Olga Taran, Joakim Tutt, Taras Holotyak, Roman Chaban, Slavi Bonev, Slava Voloshynovskiy
In the recent years, the copy detection patterns (CDP) attracted a lot of attention as a link between the physical and digital worlds, which is of great interest for the internet of things and brand protection applications. However, the security of CDP in terms of their reproducibility by unauthorized parties or clonability remains largely unexplored. In this respect, this paper addresses a problem of anti-counterfeiting of physical objects and aims at investigating the authentication aspects and the resistances to illegal copying of the modern CDP from machine learning perspectives. A special attention is paid to a reliable authentication under the real-life verification conditions when the codes are printed on an industrial printer and enrolled via modern mobile phones under regular light conditions. The theoretical and empirical investigation of authentication aspects of CDP is performed with respect to four types of copy fakes from the point of view of (i) multi-class supervised classification as a baseline approach and (ii) one-class classification as a real-life application case. The obtained results show that the modern machine-learning approaches and the technical capacities of modern mobile phones allow to reliably authenticate CDP on end-user mobile phones under the considered classes of fakes.
{"title":"Mobile authentication of copy detection patterns.","authors":"Olga Taran, Joakim Tutt, Taras Holotyak, Roman Chaban, Slavi Bonev, Slava Voloshynovskiy","doi":"10.1186/s13635-023-00140-5","DOIUrl":"10.1186/s13635-023-00140-5","url":null,"abstract":"<p><p>In the recent years, the copy detection patterns (CDP) attracted a lot of attention as a link between the physical and digital worlds, which is of great interest for the internet of things and brand protection applications. However, the security of CDP in terms of their reproducibility by unauthorized parties or clonability remains largely unexplored. In this respect, this paper addresses a problem of anti-counterfeiting of physical objects and aims at investigating the authentication aspects and the resistances to illegal copying of the modern CDP from machine learning perspectives. A special attention is paid to a reliable authentication under the real-life verification conditions when the codes are printed on an industrial printer and enrolled via modern mobile phones under regular light conditions. The theoretical and empirical investigation of authentication aspects of CDP is performed with respect to four types of copy fakes from the point of view of (i) multi-class supervised classification as a baseline approach and (ii) one-class classification as a real-life application case. The obtained results show that the modern machine-learning approaches and the technical capacities of modern mobile phones allow to reliably authenticate CDP on end-user mobile phones under the considered classes of fakes.</p>","PeriodicalId":46070,"journal":{"name":"EURASIP Journal on Information Security","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10244288/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9612480","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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