Digital content such as games,extended reality(XR),and movies has been widely and easily distributed over wireless networks.As a result,unauthorized access,copyright infringement by third parties or eavesdroppers,and ...Digital content such as games,extended reality(XR),and movies has been widely and easily distributed over wireless networks.As a result,unauthorized access,copyright infringement by third parties or eavesdroppers,and cyberattacks over these networks have become pressing concerns.Therefore,protecting copyrighted content and preventing illegal distribution in wireless communications has garnered significant attention.The Intelligent Reflecting Surface(IRS)is regarded as a promising technology for future wireless and mobile networks due to its ability to reconfigure the radio propagation environment.This study investigates the security performance of an uplink Non-Orthogonal Multiple Access(NOMA)system integrated with an IRS and employing Fountain Codes(FCs).Specifically,two users send signals to the base station at separate distances.A relay receives the signal from the nearby user first and then relays it to the base station.The IRS receives the signal from the distant user and reflects it to the relay,which then sends the reflected signal to the base station.Furthermore,a malevolent eavesdropper intercepts both user and relay communications.We construct mathematical equations for Outage Probability(OP),throughput,diversity evaluation,and Interception Probability(IP),offering quantitative insights to assess system security and performance.Additionally,OP and IP are analyzed using a Deep Neural Network(DNN)model.A deeper comprehension of the security performance of the IRS-assisted NOMA systemin signal transmission is provided by Monte Carlo simulations,which are also carried out to confirm the theoretical conclusions.展开更多
The growing sophistication of cyberthreats,among others the Distributed Denial of Service attacks,has exposed limitations in traditional rule-based Security Information and Event Management systems.While machine learn...The growing sophistication of cyberthreats,among others the Distributed Denial of Service attacks,has exposed limitations in traditional rule-based Security Information and Event Management systems.While machine learning–based intrusion detection systems can capture complex network behaviours,their“black-box”nature often limits trust and actionable insight for security operators.This study introduces a novel approach that integrates Explainable Artificial Intelligence—xAI—with the Random Forest classifier to derive human-interpretable rules,thereby enhancing the detection of Distributed Denial of Service(DDoS)attacks.The proposed framework combines traditional static rule formulation with advanced xAI techniques—SHapley Additive exPlanations and Scoped Rules-to extract decision criteria from a fully trained model.The methodology was validated on two benchmark datasets,CICIDS2017 and WUSTL-IIOT-2021.Extracted rules were evaluated against conventional Security Information and Event Management Systems rules with metrics such as precision,recall,accuracy,balanced accuracy,and Matthews Correlation Coefficient.Experimental results demonstrate that xAI-derived rules consistently outperform traditional static rules.Notably,the most refined xAI-generated rule achieved near-perfect performance with significantly improved detection of DDoS traffic while maintaining high accuracy in classifying benign traffic across both datasets.展开更多
The forthcoming 6G wireless networks have great potential for establishing AI-based networks that can enhance end-to-end connection and manage massive data of real-time networks.Artificial Intelligence(AI)advancements...The forthcoming 6G wireless networks have great potential for establishing AI-based networks that can enhance end-to-end connection and manage massive data of real-time networks.Artificial Intelligence(AI)advancements have contributed to the development of several innovative technologies by providing sophisticated specific AI mathematical models such as machine learning models,deep learning models,and hybrid models.Furthermore,intelligent resource management allows for self-configuration and autonomous decision-making capabilities of AI methods,which in turn improves the performance of 6G networks.Hence,6G networks rely substantially on AI methods to manage resources.This paper comprehensively surveys the recent work of AI methods-based resource management for 6G networks.Firstly,the AI methods are categorized into Deep Learning(DL),Federated Learning(FL),Reinforcement Learning(RL),and Evolutionary Learning(EL).Then,we analyze the AI approaches according to optimization issues such as user association,channel allocation,power allocation,and mode selection.Thereafter,we provide appropriate solutions to the most significant problems with the existing approaches of AI-based resource management.Finally,various open issues and potential trends related to AI-based resource management applications are presented.In summary,this survey enables researchers to understand these advancements thoroughly and quickly identify remaining challenges that need further investigation.展开更多
The work presents studies on the complex permittivity and permeability of composites based on acrylonitrile butadiene rubber containing combinations of conductive fillers which include carbon black and nickel powder. ...The work presents studies on the complex permittivity and permeability of composites based on acrylonitrile butadiene rubber containing combinations of conductive fillers which include carbon black and nickel powder. The properties of those composites, containing each of the fillers at the same amount were compared. The permittivity and permeability values of the composites are influenced remarkably by their morphology and structure as well as by the morphological and structural specifics of both fillers. As electron scanning microscopy studies confirm, those parameters are predetermined by the nature of the composites studied—particle size, particles arrangement in the matrix and their tendency to clustering. Last but not least matrix-filler interface phenomena also impact the characteristics in question. The possibilities for applications of the composites in antennae have been studied, in particular, as substrates and insulating layers in flexible antennae for body centric communications (BCCs). The research results allow the conclusion that these materials can find such applications indeed. Composites of higher conductivity can be used where surface waves are generated to provide on-body communications, while composites of lower conductivity may be used for antennae that will be on the body of a person and will transmit to and receive from other antennas that are not on the body of the same person (off-body communications). It is clear that one can engineer the properties of antennae substrates at microwave frequencies by adjusting the filler content and the type of filler and thus control and tailor the antenna performance specific for a particular application.展开更多
The paper presents the synthesis and characterization of carbon black/silicone dioxide hybrid fillers obtained by an impregnation technology. The electromagnetic interference shielding effectiveness of the composites ...The paper presents the synthesis and characterization of carbon black/silicone dioxide hybrid fillers obtained by an impregnation technology. The electromagnetic interference shielding effectiveness of the composites filled with carbon black/silicone dioxide hybrid fillers was measured in wide frequency range of 1 - 12 GHz. The dc and ac electrical conductivity of composites also have been investigated. The relationship between electrical (dc and ac) conductivity and shielding effectiveness was analyzed. A positive correlation was found between the absorptive shielding effectiveness and ac conductivity for composites comprising conductive carbon black/silica filler, when the filler loading is above the percolation threshold.展开更多
基金supported in part by Vietnam National Foundation for Science and Technology Development(NAFOSTED)under Grant 102.04-2021.57in part by Culture,Sports and Tourism R&D Program through the Korea Creative Content Agency grant funded by the Ministry of Culture,Sports and Tourism in 2024(Project Name:Global Talent Training Program for Copyright Management Technology in Game Contents,Project Number:RS-2024-00396709,Contribution Rate:100%).
文摘Digital content such as games,extended reality(XR),and movies has been widely and easily distributed over wireless networks.As a result,unauthorized access,copyright infringement by third parties or eavesdroppers,and cyberattacks over these networks have become pressing concerns.Therefore,protecting copyrighted content and preventing illegal distribution in wireless communications has garnered significant attention.The Intelligent Reflecting Surface(IRS)is regarded as a promising technology for future wireless and mobile networks due to its ability to reconfigure the radio propagation environment.This study investigates the security performance of an uplink Non-Orthogonal Multiple Access(NOMA)system integrated with an IRS and employing Fountain Codes(FCs).Specifically,two users send signals to the base station at separate distances.A relay receives the signal from the nearby user first and then relays it to the base station.The IRS receives the signal from the distant user and reflects it to the relay,which then sends the reflected signal to the base station.Furthermore,a malevolent eavesdropper intercepts both user and relay communications.We construct mathematical equations for Outage Probability(OP),throughput,diversity evaluation,and Interception Probability(IP),offering quantitative insights to assess system security and performance.Additionally,OP and IP are analyzed using a Deep Neural Network(DNN)model.A deeper comprehension of the security performance of the IRS-assisted NOMA systemin signal transmission is provided by Monte Carlo simulations,which are also carried out to confirm the theoretical conclusions.
基金funded under the Horizon Europe AI4CYBER Projectwhich has received funding from the European Union’s Horizon Europe Research and Innovation Programme under grant agreement No.101070450.
文摘The growing sophistication of cyberthreats,among others the Distributed Denial of Service attacks,has exposed limitations in traditional rule-based Security Information and Event Management systems.While machine learning–based intrusion detection systems can capture complex network behaviours,their“black-box”nature often limits trust and actionable insight for security operators.This study introduces a novel approach that integrates Explainable Artificial Intelligence—xAI—with the Random Forest classifier to derive human-interpretable rules,thereby enhancing the detection of Distributed Denial of Service(DDoS)attacks.The proposed framework combines traditional static rule formulation with advanced xAI techniques—SHapley Additive exPlanations and Scoped Rules-to extract decision criteria from a fully trained model.The methodology was validated on two benchmark datasets,CICIDS2017 and WUSTL-IIOT-2021.Extracted rules were evaluated against conventional Security Information and Event Management Systems rules with metrics such as precision,recall,accuracy,balanced accuracy,and Matthews Correlation Coefficient.Experimental results demonstrate that xAI-derived rules consistently outperform traditional static rules.Notably,the most refined xAI-generated rule achieved near-perfect performance with significantly improved detection of DDoS traffic while maintaining high accuracy in classifying benign traffic across both datasets.
基金funded by Universiti Kebangsaan Malaysia,Fundamental Research Grant Scheme having Grant number FRGS/1/2023/ICT07/UKM/02/1Universiti Kebangsaan Malaysia Geran Universiti Penyelidikan having Grant number GUP-2024-009.
文摘The forthcoming 6G wireless networks have great potential for establishing AI-based networks that can enhance end-to-end connection and manage massive data of real-time networks.Artificial Intelligence(AI)advancements have contributed to the development of several innovative technologies by providing sophisticated specific AI mathematical models such as machine learning models,deep learning models,and hybrid models.Furthermore,intelligent resource management allows for self-configuration and autonomous decision-making capabilities of AI methods,which in turn improves the performance of 6G networks.Hence,6G networks rely substantially on AI methods to manage resources.This paper comprehensively surveys the recent work of AI methods-based resource management for 6G networks.Firstly,the AI methods are categorized into Deep Learning(DL),Federated Learning(FL),Reinforcement Learning(RL),and Evolutionary Learning(EL).Then,we analyze the AI approaches according to optimization issues such as user association,channel allocation,power allocation,and mode selection.Thereafter,we provide appropriate solutions to the most significant problems with the existing approaches of AI-based resource management.Finally,various open issues and potential trends related to AI-based resource management applications are presented.In summary,this survey enables researchers to understand these advancements thoroughly and quickly identify remaining challenges that need further investigation.
文摘The work presents studies on the complex permittivity and permeability of composites based on acrylonitrile butadiene rubber containing combinations of conductive fillers which include carbon black and nickel powder. The properties of those composites, containing each of the fillers at the same amount were compared. The permittivity and permeability values of the composites are influenced remarkably by their morphology and structure as well as by the morphological and structural specifics of both fillers. As electron scanning microscopy studies confirm, those parameters are predetermined by the nature of the composites studied—particle size, particles arrangement in the matrix and their tendency to clustering. Last but not least matrix-filler interface phenomena also impact the characteristics in question. The possibilities for applications of the composites in antennae have been studied, in particular, as substrates and insulating layers in flexible antennae for body centric communications (BCCs). The research results allow the conclusion that these materials can find such applications indeed. Composites of higher conductivity can be used where surface waves are generated to provide on-body communications, while composites of lower conductivity may be used for antennae that will be on the body of a person and will transmit to and receive from other antennas that are not on the body of the same person (off-body communications). It is clear that one can engineer the properties of antennae substrates at microwave frequencies by adjusting the filler content and the type of filler and thus control and tailor the antenna performance specific for a particular application.
文摘The paper presents the synthesis and characterization of carbon black/silicone dioxide hybrid fillers obtained by an impregnation technology. The electromagnetic interference shielding effectiveness of the composites filled with carbon black/silicone dioxide hybrid fillers was measured in wide frequency range of 1 - 12 GHz. The dc and ac electrical conductivity of composites also have been investigated. The relationship between electrical (dc and ac) conductivity and shielding effectiveness was analyzed. A positive correlation was found between the absorptive shielding effectiveness and ac conductivity for composites comprising conductive carbon black/silica filler, when the filler loading is above the percolation threshold.
