Pub Date : 2024-04-08DOI: 10.1186/s13635-024-00155-6
Kiran Saleem, Lei Wang, Salil Bharany, Khmaies Ouahada, Ateeq Ur Rehman, Habib Hamam
The research addresses energy consumption, latency, and network reliability challenges in wireless sensor network communication, especially in military security applications. A multi-agent context-aware model employing the belief-desire-intention (BDI) reasoning mechanism is proposed. This model utilizes a semantic knowledge-based intelligent reasoning network to monitor suspicious activities within a prohibited zone, generating alerts. Additionally, a BDI intelligent multi-level data transmission routing algorithm is proposed to optimize energy consumption constraints and enhance energy-awareness among nodes. The energy optimization analysis involves the Energy Percent Dataset, showcasing the efficiency of four wireless sensor network techniques (E-FEERP, GTEB, HHO-UCRA, EEIMWSN) in maintaining high energy levels. E-FEERP consistently exhibits superior energy efficiency (93 to 98%), emphasizing its effectiveness. The Energy Consumption Dataset provides insights into the joule measurements of energy consumption for each technique, highlighting their diverse energy efficiency characteristics. Latency measurements are presented for four techniques within a fixed transmission range of 5000 m. E-FEERP demonstrates latency ranging from 3.0 to 4.0 s, while multi-hop latency values range from 2.7 to 2.9 s. These values provide valuable insights into the performance characteristics of each technique under specified conditions. The Packet Delivery Ratio (PDR) dataset reveals the consistent performance of the techniques in maintaining successful packet delivery within the specified transmission range. E-FEERP achieves PDR values between 89.5 and 92.3%, demonstrating its reliability. The Packet Received Data further illustrates the efficiency of each technique in receiving transmitted packets. Moreover the network lifetime results show E-FEERP consistently improving from 2550 s to round 925. GTEB and HHO-UCRA exhibit fluctuations around 3100 and 3600 s, indicating variable performance. In contrast, EEIMWSN consistently improves from round 1250 to 4500 s.
{"title":"Intelligent multi-agent model for energy-efficient communication in wireless sensor networks","authors":"Kiran Saleem, Lei Wang, Salil Bharany, Khmaies Ouahada, Ateeq Ur Rehman, Habib Hamam","doi":"10.1186/s13635-024-00155-6","DOIUrl":"https://doi.org/10.1186/s13635-024-00155-6","url":null,"abstract":"The research addresses energy consumption, latency, and network reliability challenges in wireless sensor network communication, especially in military security applications. A multi-agent context-aware model employing the belief-desire-intention (BDI) reasoning mechanism is proposed. This model utilizes a semantic knowledge-based intelligent reasoning network to monitor suspicious activities within a prohibited zone, generating alerts. Additionally, a BDI intelligent multi-level data transmission routing algorithm is proposed to optimize energy consumption constraints and enhance energy-awareness among nodes. The energy optimization analysis involves the Energy Percent Dataset, showcasing the efficiency of four wireless sensor network techniques (E-FEERP, GTEB, HHO-UCRA, EEIMWSN) in maintaining high energy levels. E-FEERP consistently exhibits superior energy efficiency (93 to 98%), emphasizing its effectiveness. The Energy Consumption Dataset provides insights into the joule measurements of energy consumption for each technique, highlighting their diverse energy efficiency characteristics. Latency measurements are presented for four techniques within a fixed transmission range of 5000 m. E-FEERP demonstrates latency ranging from 3.0 to 4.0 s, while multi-hop latency values range from 2.7 to 2.9 s. These values provide valuable insights into the performance characteristics of each technique under specified conditions. The Packet Delivery Ratio (PDR) dataset reveals the consistent performance of the techniques in maintaining successful packet delivery within the specified transmission range. E-FEERP achieves PDR values between 89.5 and 92.3%, demonstrating its reliability. The Packet Received Data further illustrates the efficiency of each technique in receiving transmitted packets. Moreover the network lifetime results show E-FEERP consistently improving from 2550 s to round 925. GTEB and HHO-UCRA exhibit fluctuations around 3100 and 3600 s, indicating variable performance. In contrast, EEIMWSN consistently improves from round 1250 to 4500 s.","PeriodicalId":46070,"journal":{"name":"EURASIP Journal on Information Security","volume":"37 1","pages":""},"PeriodicalIF":3.6,"publicationDate":"2024-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140582153","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 : 2024-04-04DOI: 10.1186/s13635-024-00156-5
M. Nivaashini, E. Suganya, S. Sountharrajan, M. Prabu, Durga Prasad Bavirisetti
Over the last 20 years, Wi-Fi technology has advanced to the point where most modern devices are small and rely on Wi-Fi to access the internet. Wi-Fi network security is severely questioned since there is no physical barrier separating a wireless network from a wired network, and the security procedures in place are defenseless against a wide range of threats. This study set out to assess federated learning, a new technique, as a possible remedy for privacy issues and the high expense of data collecting in network attack detection. To detect and identify cyber threats, especially in Wi-Fi networks, the research presents FEDDBN-IDS, a revolutionary intrusion detection system (IDS) that makes use of deep belief networks (DBNs) inside a federated deep learning (FDL) framework. Every device has a pre-trained DBN with stacking restricted Boltzmann machines (RBM) to learn low-dimensional characteristics from unlabelled local and private data. Later, these models are combined by a central server using federated learning (FL) to create a global model. The whole model is then enhanced by the central server with fully linked SoftMax layers to form a supervised neural network, which is then trained using publicly accessible labeled AWID datasets. Our federated technique produces a high degree of classification accuracy, ranging from 88% to 98%, according to the results of our studies.
{"title":"FEDDBN-IDS: federated deep belief network-based wireless network intrusion detection system","authors":"M. Nivaashini, E. Suganya, S. Sountharrajan, M. Prabu, Durga Prasad Bavirisetti","doi":"10.1186/s13635-024-00156-5","DOIUrl":"https://doi.org/10.1186/s13635-024-00156-5","url":null,"abstract":"Over the last 20 years, Wi-Fi technology has advanced to the point where most modern devices are small and rely on Wi-Fi to access the internet. Wi-Fi network security is severely questioned since there is no physical barrier separating a wireless network from a wired network, and the security procedures in place are defenseless against a wide range of threats. This study set out to assess federated learning, a new technique, as a possible remedy for privacy issues and the high expense of data collecting in network attack detection. To detect and identify cyber threats, especially in Wi-Fi networks, the research presents FEDDBN-IDS, a revolutionary intrusion detection system (IDS) that makes use of deep belief networks (DBNs) inside a federated deep learning (FDL) framework. Every device has a pre-trained DBN with stacking restricted Boltzmann machines (RBM) to learn low-dimensional characteristics from unlabelled local and private data. Later, these models are combined by a central server using federated learning (FL) to create a global model. The whole model is then enhanced by the central server with fully linked SoftMax layers to form a supervised neural network, which is then trained using publicly accessible labeled AWID datasets. Our federated technique produces a high degree of classification accuracy, ranging from 88% to 98%, according to the results of our studies.","PeriodicalId":46070,"journal":{"name":"EURASIP Journal on Information Security","volume":"301 1","pages":""},"PeriodicalIF":3.6,"publicationDate":"2024-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140602127","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 : 2024-03-08DOI: 10.1186/s13635-023-00147-y
Arslan Ali, Andrea Migliorati, Tiziano Bianchi, Enrico Magli
Recently, biometric recognition has become a significant field of research. The concept of cancelable biometrics (CB) has been introduced to address security concerns related to the handling of sensitive data. In this paper, we address unconstrained face verification by proposing a deep cancelable framework called BiometricNet+ that employs random projections (RP) to conceal face images and compressive sensing (CS) to reconstruct measurements in the original domain. Our lightweight design enforces the properties of unlinkability, revocability, and non-invertibility of the templates while preserving face recognition accuracy. We compare facial features by learning a regularized metric: at training time, we jointly learn facial features and the metric such that matching and non-matching pairs are mapped onto latent target distributions; then, for biometric verification, features are randomly projected via random matrices changed at every enrollment and query and reconstructed before the latent space mapping is computed. We assess the face recognition accuracy of our framework on challenging datasets such as LFW, CALFW, CPLFW, AgeDB, YTF, CFP, and RFW, showing notable improvements over state-of-the-art techniques while meeting the criteria for secure cancelable template design. Since our method requires no fine-tuning of the learned features, it can be applied to pre-trained networks to increase sensitive data protection.
{"title":"Cancelable templates for secure face verification based on deep learning and random projections","authors":"Arslan Ali, Andrea Migliorati, Tiziano Bianchi, Enrico Magli","doi":"10.1186/s13635-023-00147-y","DOIUrl":"https://doi.org/10.1186/s13635-023-00147-y","url":null,"abstract":"Recently, biometric recognition has become a significant field of research. The concept of cancelable biometrics (CB) has been introduced to address security concerns related to the handling of sensitive data. In this paper, we address unconstrained face verification by proposing a deep cancelable framework called BiometricNet+ that employs random projections (RP) to conceal face images and compressive sensing (CS) to reconstruct measurements in the original domain. Our lightweight design enforces the properties of unlinkability, revocability, and non-invertibility of the templates while preserving face recognition accuracy. We compare facial features by learning a regularized metric: at training time, we jointly learn facial features and the metric such that matching and non-matching pairs are mapped onto latent target distributions; then, for biometric verification, features are randomly projected via random matrices changed at every enrollment and query and reconstructed before the latent space mapping is computed. We assess the face recognition accuracy of our framework on challenging datasets such as LFW, CALFW, CPLFW, AgeDB, YTF, CFP, and RFW, showing notable improvements over state-of-the-art techniques while meeting the criteria for secure cancelable template design. Since our method requires no fine-tuning of the learned features, it can be applied to pre-trained networks to increase sensitive data protection.","PeriodicalId":46070,"journal":{"name":"EURASIP Journal on Information Security","volume":"64 1","pages":""},"PeriodicalIF":3.6,"publicationDate":"2024-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140075134","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 : 2024-02-24DOI: 10.1186/s13635-024-00154-7
Shengqi Wu
With the rapid development of the Internet of Things and intelligent technology, the application of Radio Frequency Identification (RFID) technology in training room management is becoming increasingly widespread. An efficient and accurate RFID system can significantly improve the management efficiency and resource utilization of the training room, thereby improving teaching quality and reducing management costs. Although RFID technology has many advantages, there are still some problems in practical applications, such as label collision and recognition of unknown labels. These issues not only affect the performance of the system but may also cause interference with actual teaching and management. This study proposes a grouping-based bit arbitration query tree algorithm and anti-collision technology to solve label collisions and reduce label recognition time in the technology. A new unknown label recognition algorithm is also proposed to improve the recognition efficiency and accuracy of identifying new unknown labels. Related experiments have shown that the recognition accuracy of the algorithm designed this time is 95.86%. Compared with other algorithms, the number of idle time slots is the smallest. When the number of queries is 1000, the algorithm has 1842 queries, and the communication complexity is the best. When the number of unknown tags is 10,000, the actual accuracy rate is 95.642%. Compared with traditional recognition algorithms, the new unknown label recognition algorithm has a smaller frame length in the same label proportion and good recognition performance. On a theoretical level, the research content on RFID technology helps to improve and develop the basic theories of the Internet of Things and intelligent recognition technology and provides solutions and application technologies for equipment management and IoT applications in training rooms. On a practical level, the research results can provide specific guidance for the management of training rooms, help solve equipment management and safety maintenance problems in practical applications, and improve the management efficiency of training rooms.
随着物联网和智能技术的快速发展，射频识别（RFID）技术在实训室管理中的应用也越来越广泛。高效、准确的 RFID 系统能显著提高实训室的管理效率和资源利用率，从而提高教学质量，降低管理成本。虽然 RFID 技术有很多优点，但在实际应用中仍存在一些问题，如标签碰撞和未知标签的识别等。这些问题不仅会影响系统性能，还可能对实际教学和管理造成干扰。本研究提出了一种基于分组的位仲裁查询树算法和防碰撞技术，以解决该技术中的标签碰撞问题并缩短标签识别时间。同时，还提出了一种新的未知标签识别算法，以提高识别效率和识别新的未知标签的准确性。相关实验表明，本次设计的算法识别准确率为 95.86%。与其他算法相比，空闲时隙数最小。当查询次数为 1000 次时，该算法的查询次数为 1842 次，通信复杂度最好。当未知标签数量为 10,000 个时，实际准确率为 95.642%。与传统识别算法相比，新的未知标签识别算法在相同标签比例下，帧长较小，识别性能良好。在理论层面，RFID 技术的研究内容有助于完善和发展物联网和智能识别技术的基础理论，为实训室设备管理和物联网应用提供解决方案和应用技术。在实践层面，研究成果可为实训室管理提供具体指导，有助于解决实际应用中的设备管理和安全维护问题，提高实训室的管理效率。
{"title":"RFID tag recognition model for Internet of Things for training room management","authors":"Shengqi Wu","doi":"10.1186/s13635-024-00154-7","DOIUrl":"https://doi.org/10.1186/s13635-024-00154-7","url":null,"abstract":"With the rapid development of the Internet of Things and intelligent technology, the application of Radio Frequency Identification (RFID) technology in training room management is becoming increasingly widespread. An efficient and accurate RFID system can significantly improve the management efficiency and resource utilization of the training room, thereby improving teaching quality and reducing management costs. Although RFID technology has many advantages, there are still some problems in practical applications, such as label collision and recognition of unknown labels. These issues not only affect the performance of the system but may also cause interference with actual teaching and management. This study proposes a grouping-based bit arbitration query tree algorithm and anti-collision technology to solve label collisions and reduce label recognition time in the technology. A new unknown label recognition algorithm is also proposed to improve the recognition efficiency and accuracy of identifying new unknown labels. Related experiments have shown that the recognition accuracy of the algorithm designed this time is 95.86%. Compared with other algorithms, the number of idle time slots is the smallest. When the number of queries is 1000, the algorithm has 1842 queries, and the communication complexity is the best. When the number of unknown tags is 10,000, the actual accuracy rate is 95.642%. Compared with traditional recognition algorithms, the new unknown label recognition algorithm has a smaller frame length in the same label proportion and good recognition performance. On a theoretical level, the research content on RFID technology helps to improve and develop the basic theories of the Internet of Things and intelligent recognition technology and provides solutions and application technologies for equipment management and IoT applications in training rooms. On a practical level, the research results can provide specific guidance for the management of training rooms, help solve equipment management and safety maintenance problems in practical applications, and improve the management efficiency of training rooms.","PeriodicalId":46070,"journal":{"name":"EURASIP Journal on Information Security","volume":"8 1","pages":""},"PeriodicalIF":3.6,"publicationDate":"2024-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139957042","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 : 2024-02-24DOI: 10.1186/s13635-024-00152-9
Zhu Lan
With the development of Internet technology, the large number of network nodes and dynamic structure makes network security detection more complex, which requires the use of a multi-layer feedforward neural network to build a security threat detection model to improve network security protection. Therefore, the entropy model is adopted to optimize the particle swarm algorithm to decode particles, and then the single-peak and multi-peak functions are used to test and compare the particle entropy and fitness values to optimize the weights and thresholds in the multi-layer feedforward neural network. Finally, Suspicious Network Event Recognition Dataset discovered by data mining is sampled and applied to the entropy model particle swarm optimization for training. The test results show that there are four functions for the optimal mean and standard deviation in this algorithm, with values of 5.712e − 02, 4.805e − 02, 4.914e − 01, 1.066e − 01, 1.577e − 01, 1.343e − 01, and 2.089e + 01, 5.926, respectively. Overall, the algorithm proposed in the study is the best. Finally, the detection rate of attack types is calculated. The multi-layer feedforward neural network algorithm is 83.80%, the particle swarm optimization neural network algorithm is 91.00%, and the entropy model particle swarm optimization algorithm is 95.00%. The experiment shows that the research model has high accuracy in detecting network security threats, which can provide technical support and theoretical assistance for network security protection.
{"title":"Network security threat detection technology based on EPSO-BP algorithm","authors":"Zhu Lan","doi":"10.1186/s13635-024-00152-9","DOIUrl":"https://doi.org/10.1186/s13635-024-00152-9","url":null,"abstract":"With the development of Internet technology, the large number of network nodes and dynamic structure makes network security detection more complex, which requires the use of a multi-layer feedforward neural network to build a security threat detection model to improve network security protection. Therefore, the entropy model is adopted to optimize the particle swarm algorithm to decode particles, and then the single-peak and multi-peak functions are used to test and compare the particle entropy and fitness values to optimize the weights and thresholds in the multi-layer feedforward neural network. Finally, Suspicious Network Event Recognition Dataset discovered by data mining is sampled and applied to the entropy model particle swarm optimization for training. The test results show that there are four functions for the optimal mean and standard deviation in this algorithm, with values of 5.712e − 02, 4.805e − 02, 4.914e − 01, 1.066e − 01, 1.577e − 01, 1.343e − 01, and 2.089e + 01, 5.926, respectively. Overall, the algorithm proposed in the study is the best. Finally, the detection rate of attack types is calculated. The multi-layer feedforward neural network algorithm is 83.80%, the particle swarm optimization neural network algorithm is 91.00%, and the entropy model particle swarm optimization algorithm is 95.00%. The experiment shows that the research model has high accuracy in detecting network security threats, which can provide technical support and theoretical assistance for network security protection.","PeriodicalId":46070,"journal":{"name":"EURASIP Journal on Information Security","volume":"33 1","pages":""},"PeriodicalIF":3.6,"publicationDate":"2024-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139956255","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 : 2024-02-22DOI: 10.1186/s13635-024-00153-8
Lin Yang
In recent years, various smart devices based on IoT technology, such as smart homes, healthcare, detection, and logistics systems, have emerged. However, as the number of IoT-connected devices increases, securing the IoT is becoming increasingly challenging. To tackle the increasing security challenges caused by the proliferation of IoT devices, this research proposes an innovative method for IoT identity authentication. The method is based on an improved ring-learning with errors (R-LWE) algorithm, which encrypts and decrypts communication between devices and servers effectively using polynomial modular multiplication and modular addition operations. The main innovation of this study is the improvement of the traditional R-LWE algorithm, enhancing its efficiency and security. Experimental results demonstrated that, when compared to number theory-based algorithms and elliptic curve cryptography algorithms at a 256-bit security level, the enhanced algorithm achieves significant advantages. The improved algorithm encrypted 20 data points with an average runtime of only 3.6 ms, compared to 7.3 ms and 7.7 ms for the other algorithms. Similarly, decrypting the same amount of data had an average runtime of 2.9 ms, as opposed to 7.3 ms and 8 ms for the other algorithms. Additionally, the improved R-LWE algorithm had significant advantages in terms of communication and storage costs. Compared to the number theory-based algorithm, the R-LWE algorithm reduced communication and storage costs by 3 °C each, and compared to elliptic curve cryptography, it reduced them by 4 °C each. This achievement not only enhances the efficiency of encryption and decryption but also lowers the overall operational costs of the algorithm. The research has made significant strides in improving the security and efficiency of IoT device identity authentication by enhancing the R-LWE algorithm. This study provides theoretical and practical foundations for the development and application of related technologies, as well as new solutions for IoT security.
{"title":"Efficient identity security authentication method based on improved R-LWE algorithm in IoT environment","authors":"Lin Yang","doi":"10.1186/s13635-024-00153-8","DOIUrl":"https://doi.org/10.1186/s13635-024-00153-8","url":null,"abstract":"In recent years, various smart devices based on IoT technology, such as smart homes, healthcare, detection, and logistics systems, have emerged. However, as the number of IoT-connected devices increases, securing the IoT is becoming increasingly challenging. To tackle the increasing security challenges caused by the proliferation of IoT devices, this research proposes an innovative method for IoT identity authentication. The method is based on an improved ring-learning with errors (R-LWE) algorithm, which encrypts and decrypts communication between devices and servers effectively using polynomial modular multiplication and modular addition operations. The main innovation of this study is the improvement of the traditional R-LWE algorithm, enhancing its efficiency and security. Experimental results demonstrated that, when compared to number theory-based algorithms and elliptic curve cryptography algorithms at a 256-bit security level, the enhanced algorithm achieves significant advantages. The improved algorithm encrypted 20 data points with an average runtime of only 3.6 ms, compared to 7.3 ms and 7.7 ms for the other algorithms. Similarly, decrypting the same amount of data had an average runtime of 2.9 ms, as opposed to 7.3 ms and 8 ms for the other algorithms. Additionally, the improved R-LWE algorithm had significant advantages in terms of communication and storage costs. Compared to the number theory-based algorithm, the R-LWE algorithm reduced communication and storage costs by 3 °C each, and compared to elliptic curve cryptography, it reduced them by 4 °C each. This achievement not only enhances the efficiency of encryption and decryption but also lowers the overall operational costs of the algorithm. The research has made significant strides in improving the security and efficiency of IoT device identity authentication by enhancing the R-LWE algorithm. This study provides theoretical and practical foundations for the development and application of related technologies, as well as new solutions for IoT security.","PeriodicalId":46070,"journal":{"name":"EURASIP Journal on Information Security","volume":"2 1","pages":""},"PeriodicalIF":3.6,"publicationDate":"2024-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139925994","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 : 2024-01-31DOI: 10.1186/s13635-024-00151-w
Kongze Li
The development of big data has epromoted the development of Internet technology, but it has brought more network security and privacy problems. Therefore, how to solve network security problems is the main research direction of current network technology development. In order to deal with the harm of network attacks to personal privacy security, this paper studies and proposes an RFID mutual authentication protocol against exhaustive attacks based on improved Hash function, and proposes a security proof based on BAN logic rules. At the same time, to enhance the computing resources of the improved protocol, this paper proposes an improved authentication query protocol for multi-source RFID tags. In the performance analysis, when the distance between the reader and the tag reaches 10 m, the improved protocol can still be higher than 90%. The application test shows that the improved protocol proposed in the study is capable of resisting exhaustive attacks, its execution time is short, and it is less affected by the number of tags. The above results show that in the context of big data, the improved RFID mutual authentication protocol proposed by the research against network exhaustive attacks has a more significant defense effect, can effectively protect user privacy, and has a greater reference value in network security research.
大数据的发展推动了互联网技术的发展，但也带来了更多的网络安全和隐私问题。因此，如何解决网络安全问题是当前网络技术发展的主要研究方向。针对网络攻击对个人隐私安全的危害，本文研究并提出了一种基于改进哈希函数的 RFID 互认证协议，并提出了基于 BAN 逻辑规则的安全证明。同时，为了提高改进协议的计算资源，本文提出了一种针对多源 RFID 标签的改进认证查询协议。在性能分析中，当读写器与标签之间的距离达到 10 m 时，改进协议的识别率仍能高于 90%。应用测试表明，本研究提出的改进协议能够抵御穷举攻击，执行时间短，受标签数量的影响较小。以上结果表明，在大数据背景下，研究提出的针对网络穷举攻击的改进RFID相互认证协议具有较为显著的防御效果，能有效保护用户隐私，在网络安全研究中具有较大的参考价值。
{"title":"Improved RFID mutual authentication protocol against exhaustive attack in the context of big data","authors":"Kongze Li","doi":"10.1186/s13635-024-00151-w","DOIUrl":"https://doi.org/10.1186/s13635-024-00151-w","url":null,"abstract":"The development of big data has epromoted the development of Internet technology, but it has brought more network security and privacy problems. Therefore, how to solve network security problems is the main research direction of current network technology development. In order to deal with the harm of network attacks to personal privacy security, this paper studies and proposes an RFID mutual authentication protocol against exhaustive attacks based on improved Hash function, and proposes a security proof based on BAN logic rules. At the same time, to enhance the computing resources of the improved protocol, this paper proposes an improved authentication query protocol for multi-source RFID tags. In the performance analysis, when the distance between the reader and the tag reaches 10 m, the improved protocol can still be higher than 90%. The application test shows that the improved protocol proposed in the study is capable of resisting exhaustive attacks, its execution time is short, and it is less affected by the number of tags. The above results show that in the context of big data, the improved RFID mutual authentication protocol proposed by the research against network exhaustive attacks has a more significant defense effect, can effectively protect user privacy, and has a greater reference value in network security research.","PeriodicalId":46070,"journal":{"name":"EURASIP Journal on Information Security","volume":"34 1","pages":""},"PeriodicalIF":3.6,"publicationDate":"2024-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139648741","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 : 2024-01-02DOI: 10.1186/s13635-023-00150-3
Jialu Lv
Personalized medical data privacy and secure storage protection face serious challenges, especially in terms of data security and storage efficiency. Traditional encryption and storage solutions cannot meet the needs of modern medical data protection, which has led to an urgent need for new data protection strategies. Research personalized medical data privacy and secure storage protection based on hybrid encryption, in order to improve the security and efficiency of data storage. A hybrid encryption mechanism was proposed, which uses user attributes as keys for data encryption. The results show that the storage consumption of user attribute keys increases with the number of user attributes, but the consumption of hybrid encryption privacy storage technology is much smaller than that of traditional schemes. In the test, when the number of users increased to 30, the processing time first reached 1200 ms. During the increase in data volume, both test data and real data showed a brief decrease in attack frequency, but after the data volume reached 730–780, the attack frequency increased. It is worth noting that the performance of test data is better than that of real data. Personalized medical data privacy and secure storage protection based on hybrid encryption can not only effectively improve data security and reduce the risk of attack, but also greatly outperform traditional solutions in storage consumption and processing time. It has important practical significance for modern medical data storage protection.
{"title":"Research on privacy and secure storage protection of personalized medical data based on hybrid encryption","authors":"Jialu Lv","doi":"10.1186/s13635-023-00150-3","DOIUrl":"https://doi.org/10.1186/s13635-023-00150-3","url":null,"abstract":"Personalized medical data privacy and secure storage protection face serious challenges, especially in terms of data security and storage efficiency. Traditional encryption and storage solutions cannot meet the needs of modern medical data protection, which has led to an urgent need for new data protection strategies. Research personalized medical data privacy and secure storage protection based on hybrid encryption, in order to improve the security and efficiency of data storage. A hybrid encryption mechanism was proposed, which uses user attributes as keys for data encryption. The results show that the storage consumption of user attribute keys increases with the number of user attributes, but the consumption of hybrid encryption privacy storage technology is much smaller than that of traditional schemes. In the test, when the number of users increased to 30, the processing time first reached 1200 ms. During the increase in data volume, both test data and real data showed a brief decrease in attack frequency, but after the data volume reached 730–780, the attack frequency increased. It is worth noting that the performance of test data is better than that of real data. Personalized medical data privacy and secure storage protection based on hybrid encryption can not only effectively improve data security and reduce the risk of attack, but also greatly outperform traditional solutions in storage consumption and processing time. It has important practical significance for modern medical data storage protection.","PeriodicalId":46070,"journal":{"name":"EURASIP Journal on Information Security","volume":"34 1","pages":""},"PeriodicalIF":3.6,"publicationDate":"2024-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139083874","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 : 2024-01-02DOI: 10.1186/s13635-023-00145-0
Zhiqiang Chen, Zhihua Song, Tao Zhang, Yong Wei
Extensive research has been conducted to enhance the availability of IoT devices and data by focusing on the rapid prediction of instantaneous fault rates and temperatures. Temperature plays a crucial role in device availability as it significantly impacts equipment performance and lifespan. It serves as a vital indicator for predicting equipment failure and enables the improvement of availability and efficiency through effective temperature management. In the proposed optimization scheme for IoT device and data availability, the artificial neural network (ANN) algorithm and the K-Nearest Neighbours (KNN) algorithm are utilized to drive a neural network. The preliminary algorithm for availability optimization is chosen, and the target is divided into two parts: data optimization and equipment optimization. Suitable models are constructed for each part, and the KNN-driven neural network algorithm is employed to solve the proposed optimization model. The effectiveness of the proposed scheme is clearly demonstrated by the verification results. When compared to the benchmark method, the availability forward fault-tolerant method, and the heuristic optimization algorithm, the maximum temperature was successfully reduced to 2.0750 °C. Moreover, significant enhancements in the average availability of IoT devices were achieved, with improvements of 27.03%, 15.76%, and 10.85% respectively compared to the aforementioned methods. The instantaneous failure rates were 100%, 87.89%, and 84.4% respectively for the three algorithms. This optimization algorithm proves highly efficient in eliminating fault signals and optimizing the prediction of time-limited satisfaction. Furthermore, it exhibits strategic foresight in the decision-making process.
{"title":"IoT devices and data availability optimization by ANN and KNN","authors":"Zhiqiang Chen, Zhihua Song, Tao Zhang, Yong Wei","doi":"10.1186/s13635-023-00145-0","DOIUrl":"https://doi.org/10.1186/s13635-023-00145-0","url":null,"abstract":"Extensive research has been conducted to enhance the availability of IoT devices and data by focusing on the rapid prediction of instantaneous fault rates and temperatures. Temperature plays a crucial role in device availability as it significantly impacts equipment performance and lifespan. It serves as a vital indicator for predicting equipment failure and enables the improvement of availability and efficiency through effective temperature management. In the proposed optimization scheme for IoT device and data availability, the artificial neural network (ANN) algorithm and the K-Nearest Neighbours (KNN) algorithm are utilized to drive a neural network. The preliminary algorithm for availability optimization is chosen, and the target is divided into two parts: data optimization and equipment optimization. Suitable models are constructed for each part, and the KNN-driven neural network algorithm is employed to solve the proposed optimization model. The effectiveness of the proposed scheme is clearly demonstrated by the verification results. When compared to the benchmark method, the availability forward fault-tolerant method, and the heuristic optimization algorithm, the maximum temperature was successfully reduced to 2.0750 °C. Moreover, significant enhancements in the average availability of IoT devices were achieved, with improvements of 27.03%, 15.76%, and 10.85% respectively compared to the aforementioned methods. The instantaneous failure rates were 100%, 87.89%, and 84.4% respectively for the three algorithms. This optimization algorithm proves highly efficient in eliminating fault signals and optimizing the prediction of time-limited satisfaction. Furthermore, it exhibits strategic foresight in the decision-making process.","PeriodicalId":46070,"journal":{"name":"EURASIP Journal on Information Security","volume":"15 1","pages":""},"PeriodicalIF":3.6,"publicationDate":"2024-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139084180","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 : 2023-12-13DOI: 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":"70 1","pages":""},"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}
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