Pub Date : 2024-12-01DOI: 10.23919/JCN.2024.000066
Xianli Sun;Linghua Zhang;Qiqing Zhai;Peng Zheng
How to comprehend the relationship between information spreading and individual behavior adoption is an essential problem in complex networks. To this end, a novel two-layer model to depict the diffusion of green behavior under the impact of positive and negative information is proposed. Positive information motivates people to adopt green behavior, while negative information reduces the adoption of green behavior. In the model, the physical contact layer describes the green behavior diffusion, and the information layer describes the positive and negative information spreading. Moreover, the social interactions of individuals in two layers change with time and are illustrated by an activity-driven model. Then, we develop the probability transition equations and derive the green behavior threshold. Next, experiments are carried out to confirm the preciseness and theoretical predictions of the new model. It reveals that the prevalence of green behavior can be promoted by restraining the negative information transmission rate and recovery rate of the green nodes while facilitating the positive information transmission rate and green behavior transmission rate. Additionally, reducing the positive information recovery rate and the recovery rate of the green nodes, and increasing the rates of forgetting negative information are beneficial for encouraging the outbreak of green behavior. Furthermore, in the physical contact layer, higher contact capacity and greater activity heterogeneity significantly facilitate green behavior spreading. In the information layer, smaller contact capacity and weaker activity heterogeneity promote diffusion when negative information dominates, whereas larger contact capacity and stronger activity heterogeneity are beneficial when positive information prevails.
{"title":"Green behavior diffusion with positive and negative information in time-varying multiplex networks","authors":"Xianli Sun;Linghua Zhang;Qiqing Zhai;Peng Zheng","doi":"10.23919/JCN.2024.000066","DOIUrl":"https://doi.org/10.23919/JCN.2024.000066","url":null,"abstract":"How to comprehend the relationship between information spreading and individual behavior adoption is an essential problem in complex networks. To this end, a novel two-layer model to depict the diffusion of green behavior under the impact of positive and negative information is proposed. Positive information motivates people to adopt green behavior, while negative information reduces the adoption of green behavior. In the model, the physical contact layer describes the green behavior diffusion, and the information layer describes the positive and negative information spreading. Moreover, the social interactions of individuals in two layers change with time and are illustrated by an activity-driven model. Then, we develop the probability transition equations and derive the green behavior threshold. Next, experiments are carried out to confirm the preciseness and theoretical predictions of the new model. It reveals that the prevalence of green behavior can be promoted by restraining the negative information transmission rate and recovery rate of the green nodes while facilitating the positive information transmission rate and green behavior transmission rate. Additionally, reducing the positive information recovery rate and the recovery rate of the green nodes, and increasing the rates of forgetting negative information are beneficial for encouraging the outbreak of green behavior. Furthermore, in the physical contact layer, higher contact capacity and greater activity heterogeneity significantly facilitate green behavior spreading. In the information layer, smaller contact capacity and weaker activity heterogeneity promote diffusion when negative information dominates, whereas larger contact capacity and stronger activity heterogeneity are beneficial when positive information prevails.","PeriodicalId":54864,"journal":{"name":"Journal of Communications and Networks","volume":"26 6","pages":"650-665"},"PeriodicalIF":2.9,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10834484","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142937881","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-01DOI: 10.23919/JCN.2024.000076
{"title":"Open access publishing agreement","authors":"","doi":"10.23919/JCN.2024.000076","DOIUrl":"https://doi.org/10.23919/JCN.2024.000076","url":null,"abstract":"","PeriodicalId":54864,"journal":{"name":"Journal of Communications and Networks","volume":"26 6","pages":"716-718"},"PeriodicalIF":2.9,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10834495","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142937982","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-01DOI: 10.23919/JCN.2024.000053
Shixun Wu;Miao Zhang;Kanapathippillai Cumanan;Kai Xu;Zhangli Lan
In this paper, mobile terminal (MT) tracking based on time of arrival (TOA), angle of departure (AOD), and angle of arrival (AOA) measurements with one base station is investigated. The main challenge is the unknown propagation environment, such as line-of-sight (LOS), non-line-of-sight (NLOS) modeled as one-bounce scattering or mixed LOS/NLOS propagations, which may result in heterogeneous measurements. For LOS scenario, a linear Kalman filter (LKF) algorithm is adopted through analyzing and deriving the estimated error of MT. For NLOS scenario, as the position of scatterer is unknown, a nonlinear range equation is formulated to measure the actual AOD/AOA measurements and the position of scatterer, and three different algorithms: The extended Kalman filter (EKF), unscented Kalman filter (UKF) and an approximated LKF are developed. For mixed LOS/NLOS scenario, the modified interacting multiple model LKF (M-IMM-LKF) and the identified LKF algorithms (I-LKF) are utilized to address the issue of the frequent transition between LOS and NLOS propagations. In comparison with EKF and UKF algorithms, the simulation results and running time comparisons show the superiority and effectiveness of the LKF algorithm in LOS and NLOS scenarios. Both M-IMM-LKF and I-LKF algorithms are capable to significantly reduce the localization errors, and better than three existing algorithms.
{"title":"Single base station tracking approaches with hybrid TOA/AOD/AOA measurements in different propagation environments","authors":"Shixun Wu;Miao Zhang;Kanapathippillai Cumanan;Kai Xu;Zhangli Lan","doi":"10.23919/JCN.2024.000053","DOIUrl":"https://doi.org/10.23919/JCN.2024.000053","url":null,"abstract":"In this paper, mobile terminal (MT) tracking based on time of arrival (TOA), angle of departure (AOD), and angle of arrival (AOA) measurements with one base station is investigated. The main challenge is the unknown propagation environment, such as line-of-sight (LOS), non-line-of-sight (NLOS) modeled as one-bounce scattering or mixed LOS/NLOS propagations, which may result in heterogeneous measurements. For LOS scenario, a linear Kalman filter (LKF) algorithm is adopted through analyzing and deriving the estimated error of MT. For NLOS scenario, as the position of scatterer is unknown, a nonlinear range equation is formulated to measure the actual AOD/AOA measurements and the position of scatterer, and three different algorithms: The extended Kalman filter (EKF), unscented Kalman filter (UKF) and an approximated LKF are developed. For mixed LOS/NLOS scenario, the modified interacting multiple model LKF (M-IMM-LKF) and the identified LKF algorithms (I-LKF) are utilized to address the issue of the frequent transition between LOS and NLOS propagations. In comparison with EKF and UKF algorithms, the simulation results and running time comparisons show the superiority and effectiveness of the LKF algorithm in LOS and NLOS scenarios. Both M-IMM-LKF and I-LKF algorithms are capable to significantly reduce the localization errors, and better than three existing algorithms.","PeriodicalId":54864,"journal":{"name":"Journal of Communications and Networks","volume":"26 6","pages":"617-631"},"PeriodicalIF":2.9,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10834482","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142938131","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-01DOI: 10.23919/JCN.2024.000067
Li Zhang;Gang Zhou;Gangyin Sun;Chaopeng Wu
More and more deep learning methods have been applied to wireless communication systems. However, the collection of authentic signal data poses challenges. Moreover, due to the vulnerability of neural networks, adversarial attacks seriously threaten the security of communication systems based on deep learning models. Traditional signal augmentation methods expand the dataset through transformations such as rotation and flip, but these methods improve the adversarial robustness of the model little. However, common methods to improve adversarial robustness such as adversarial training not only have a high computational overhead but also potentially lead to a decrease in accuracy on clean samples. In this work, we propose a signal augmentation method called adversarial and mixed-based signal augmentation (AMSA). The method can improve the adversarial robustness of the model while expanding the dataset and does not compromise the generalization ability. It combines adversarial training with data mixing and then interpolates selected pairs of samples to form new samples in an expanded dataset consisting of original and adversarial samples thus generating more diverse data. We conduct experiments on the RML2016.10a and RML2018.01a datasets using automatic modulation recognition (AMR) models based on convolutional neural networks (CNN), long short-term memory (LSTM), convolutional long short-term deep neural networks (CLDNN), and transformer. And compare the performance in scenarios with different numbers of samples. The results show that AMSA allows the model to achieve comparable or even better adversarial robustness than using adversarial training, and reduces the degradation of the model's generalization performance on clean data.
{"title":"Signal augmentation method based on mixing and adversarial training for better robustness and generalization","authors":"Li Zhang;Gang Zhou;Gangyin Sun;Chaopeng Wu","doi":"10.23919/JCN.2024.000067","DOIUrl":"https://doi.org/10.23919/JCN.2024.000067","url":null,"abstract":"More and more deep learning methods have been applied to wireless communication systems. However, the collection of authentic signal data poses challenges. Moreover, due to the vulnerability of neural networks, adversarial attacks seriously threaten the security of communication systems based on deep learning models. Traditional signal augmentation methods expand the dataset through transformations such as rotation and flip, but these methods improve the adversarial robustness of the model little. However, common methods to improve adversarial robustness such as adversarial training not only have a high computational overhead but also potentially lead to a decrease in accuracy on clean samples. In this work, we propose a signal augmentation method called adversarial and mixed-based signal augmentation (AMSA). The method can improve the adversarial robustness of the model while expanding the dataset and does not compromise the generalization ability. It combines adversarial training with data mixing and then interpolates selected pairs of samples to form new samples in an expanded dataset consisting of original and adversarial samples thus generating more diverse data. We conduct experiments on the RML2016.10a and RML2018.01a datasets using automatic modulation recognition (AMR) models based on convolutional neural networks (CNN), long short-term memory (LSTM), convolutional long short-term deep neural networks (CLDNN), and transformer. And compare the performance in scenarios with different numbers of samples. The results show that AMSA allows the model to achieve comparable or even better adversarial robustness than using adversarial training, and reduces the degradation of the model's generalization performance on clean data.","PeriodicalId":54864,"journal":{"name":"Journal of Communications and Networks","volume":"26 6","pages":"679-688"},"PeriodicalIF":2.9,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10834486","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142938135","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-01DOI: 10.23919/JCN.2024.000069
Pengxue Liu;Yitong Li;Dalong Zhang
This paper presents an extensive analysis of the IEEE 802.11ax uplink orthogonal frequency-division multiple access (OFDMA)-based random access (UORA) mechanism, addressing inherent inefficiencies in channel access under varying network loads. Specifically, a mathematical model is developed to analyze the system performance of the 802.11ax UORA protocol, enabling the characterization of steady-state operating points under both saturated and unsaturated conditions. Key performance metrics, including system efficiency and mean access delay, are derived as functions of the steady-state operating points. Optimization of these performance metrics through the appropriate selection of backoff parameters is explored, with the analysis validated by simulation results. Additionally, the effects of access parameter heterogeneity, multi-link operation (MLO) and multiple resource unit (MRU) operation capabilities on the performance of IEEE 802.11ax UORA mechanism are further discussed.
{"title":"Performance optimization of IEEE 802.11ax UL OFDMA random access","authors":"Pengxue Liu;Yitong Li;Dalong Zhang","doi":"10.23919/JCN.2024.000069","DOIUrl":"https://doi.org/10.23919/JCN.2024.000069","url":null,"abstract":"This paper presents an extensive analysis of the IEEE 802.11ax uplink orthogonal frequency-division multiple access (OFDMA)-based random access (UORA) mechanism, addressing inherent inefficiencies in channel access under varying network loads. Specifically, a mathematical model is developed to analyze the system performance of the 802.11ax UORA protocol, enabling the characterization of steady-state operating points under both saturated and unsaturated conditions. Key performance metrics, including system efficiency and mean access delay, are derived as functions of the steady-state operating points. Optimization of these performance metrics through the appropriate selection of backoff parameters is explored, with the analysis validated by simulation results. Additionally, the effects of access parameter heterogeneity, multi-link operation (MLO) and multiple resource unit (MRU) operation capabilities on the performance of IEEE 802.11ax UORA mechanism are further discussed.","PeriodicalId":54864,"journal":{"name":"Journal of Communications and Networks","volume":"26 6","pages":"580-592"},"PeriodicalIF":2.9,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10834483","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142938129","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-01DOI: 10.23919/JCN.2024.000074
This index covers all papers that appeared in JCN during 2024. The Author Index contains the primary entry for each item, listed under the first author's name, and cross-references from all coauthors. The Title Index contains paper titles for each Division in the alphabetical order from No. 1 to No. 6. Please refer to the primary entry in the Author Index for the exact title, coauthors, and comments / corrections.
{"title":"2024 Index journal of communications and networks, volume 26","authors":"","doi":"10.23919/JCN.2024.000074","DOIUrl":"https://doi.org/10.23919/JCN.2024.000074","url":null,"abstract":"This index covers all papers that appeared in JCN during 2024. The Author Index contains the primary entry for each item, listed under the first author's name, and cross-references from all coauthors. The Title Index contains paper titles for each Division in the alphabetical order from No. 1 to No. 6. Please refer to the primary entry in the Author Index for the exact title, coauthors, and comments / corrections.","PeriodicalId":54864,"journal":{"name":"Journal of Communications and Networks","volume":"26 6","pages":"1-5"},"PeriodicalIF":2.9,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10834494","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142938130","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-01DOI: 10.23919/JCN.2024.000075
{"title":"Information for authors","authors":"","doi":"10.23919/JCN.2024.000075","DOIUrl":"https://doi.org/10.23919/JCN.2024.000075","url":null,"abstract":"","PeriodicalId":54864,"journal":{"name":"Journal of Communications and Networks","volume":"26 6","pages":"711-715"},"PeriodicalIF":2.9,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10834485","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142937981","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-01DOI: 10.23919/JCN.2024.000068
Ming Chen;Liang Jin;Zheng Wan;Zheyuan Deng;Bo Zhang;Yajun Chen;Kaizhi Huang
In this study, we designed a single-channel direction-of-arrival (DOA) estimation and signal separation algorithm based on a grouping scheme for the time-varying metasurfaces (TVMs) for integrated sensing and communication systems. In this scheme, the harmonic effect of the TVM was used to transform the received single-channel signal into a multichannel signal through an orthogonal Fourier coefficient matrix. After achieving multi-channel DOA estimation, the corresponding weighted beam pointing was designed for signal separation. By applying a periodic modulation function on the TVM to modulate the incident signal, the signal was mapped to a multi-dimensional received space to recover the multi-channel received signal. Thus, conventional multi-channel algorithms could be used on the single-channel signal for DOA estimation. Next, we designed a sub-surface weighted beam pointing to maximize the received signal signal-to-interference-plus-noise ratio. Simulation results revealed that the proposed scheme of DOA estimation can exhibit performances comparable to that of the conventional multichannel antenna array. Moreover, the signal separation scheme designed based on this method was robust and could maintain good signal separation ability under a low signal-to-noise ratio.
{"title":"Robust direction-of-arrival estimation and signal separation method for integrated sensing and communication","authors":"Ming Chen;Liang Jin;Zheng Wan;Zheyuan Deng;Bo Zhang;Yajun Chen;Kaizhi Huang","doi":"10.23919/JCN.2024.000068","DOIUrl":"https://doi.org/10.23919/JCN.2024.000068","url":null,"abstract":"In this study, we designed a single-channel direction-of-arrival (DOA) estimation and signal separation algorithm based on a grouping scheme for the time-varying metasurfaces (TVMs) for integrated sensing and communication systems. In this scheme, the harmonic effect of the TVM was used to transform the received single-channel signal into a multichannel signal through an orthogonal Fourier coefficient matrix. After achieving multi-channel DOA estimation, the corresponding weighted beam pointing was designed for signal separation. By applying a periodic modulation function on the TVM to modulate the incident signal, the signal was mapped to a multi-dimensional received space to recover the multi-channel received signal. Thus, conventional multi-channel algorithms could be used on the single-channel signal for DOA estimation. Next, we designed a sub-surface weighted beam pointing to maximize the received signal signal-to-interference-plus-noise ratio. Simulation results revealed that the proposed scheme of DOA estimation can exhibit performances comparable to that of the conventional multichannel antenna array. Moreover, the signal separation scheme designed based on this method was robust and could maintain good signal separation ability under a low signal-to-noise ratio.","PeriodicalId":54864,"journal":{"name":"Journal of Communications and Networks","volume":"26 6","pages":"689-698"},"PeriodicalIF":2.9,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10834481","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142938136","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-01DOI: 10.23919/JCN.2024.000073
{"title":"Reviewer list for 2024","authors":"","doi":"10.23919/JCN.2024.000073","DOIUrl":"https://doi.org/10.23919/JCN.2024.000073","url":null,"abstract":"","PeriodicalId":54864,"journal":{"name":"Journal of Communications and Networks","volume":"26 6","pages":"700-705"},"PeriodicalIF":2.9,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10834489","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142938128","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-01DOI: 10.23919/JCN.2024.000054
Imtiaz Ali Soomro;Hamood ur Rehman Khan;Syed Jawad Hussain;Zeeshan Ashraf;Mrim M. Alnfiai;Nouf Nawar Alotaibi
The emergence of Industry 4.0 entails extensive reliance on industrial cyber-physical systems (ICPS). ICPS promises to revolutionize industries by fusing physical systems with computational functionality. However, this potential increase in ICPS makes them prone to cyber threats, necessitating effective intrusion detection systems (IDS) systems. Privacy provision, system complexity, and system scalability are major challenges in IDS research. We present FedSecureIDS, a novel lightweight federated deep intrusion detection system that combines CNNs, LSTMs, MLPs, and federated learning (FL) to overcome these challenges. FedSecureIDS solves major security issues, namely eavesdropping and man-in-the-middle attacks, by employing a simple protocol for symmetric session key exchange and mutual authentication. Our Experimental results demonstrate that the proposed method is effective with an accuracy of 98.68%, precision of 98.78%, recall of 98.64%, and an F1-score of 99.05% with different edge devices. The model is similarly performed in conventional centralized IDS models. We also carry out formal security evaluations to confirm the resistance of the proposed framework to known attacks and provisioning of high data privacy and security.
{"title":"Lightweight privacy-preserving federated deep intrusion detection for industrial cyber-physical system","authors":"Imtiaz Ali Soomro;Hamood ur Rehman Khan;Syed Jawad Hussain;Zeeshan Ashraf;Mrim M. Alnfiai;Nouf Nawar Alotaibi","doi":"10.23919/JCN.2024.000054","DOIUrl":"https://doi.org/10.23919/JCN.2024.000054","url":null,"abstract":"The emergence of Industry 4.0 entails extensive reliance on industrial cyber-physical systems (ICPS). ICPS promises to revolutionize industries by fusing physical systems with computational functionality. However, this potential increase in ICPS makes them prone to cyber threats, necessitating effective intrusion detection systems (IDS) systems. Privacy provision, system complexity, and system scalability are major challenges in IDS research. We present FedSecureIDS, a novel lightweight federated deep intrusion detection system that combines CNNs, LSTMs, MLPs, and federated learning (FL) to overcome these challenges. FedSecureIDS solves major security issues, namely eavesdropping and man-in-the-middle attacks, by employing a simple protocol for symmetric session key exchange and mutual authentication. Our Experimental results demonstrate that the proposed method is effective with an accuracy of 98.68%, precision of 98.78%, recall of 98.64%, and an F1-score of 99.05% with different edge devices. The model is similarly performed in conventional centralized IDS models. We also carry out formal security evaluations to confirm the resistance of the proposed framework to known attacks and provisioning of high data privacy and security.","PeriodicalId":54864,"journal":{"name":"Journal of Communications and Networks","volume":"26 6","pages":"632-649"},"PeriodicalIF":2.9,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10834496","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142938132","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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