The ongoing expansion of the Internet of Things(IoT)fundamentally alters industrial and economic paradigms by integrating intelligent nodes throughout operational frameworks.Nonetheless,vulnerabilities surrounding sys...The ongoing expansion of the Internet of Things(IoT)fundamentally alters industrial and economic paradigms by integrating intelligent nodes throughout operational frameworks.Nonetheless,vulnerabilities surrounding system integrity and data confidentiality present major bottlenecks to widespread adoption,a dilemma severely intensified by impending quantum computing capabilities.Defending these networks demands the integration of post-quantum cryptographic primitives;yet,the severe hardware constraints characterizing peripheral IoT components complicate practical deployment.Quantum-resistant lattice cryptography offers a highly promising pathway to overcome these limitations,largely because the foundational security and throughput of these protocols hinge on polynomial multiplication performance.Consequently,optimizing the computational speed and architectural efficiency of this specific algebraic operation drastically enhances the viability of lattice-reliant defense mechanisms.To address this need,this study develops a specialized systolic array architecture engineered explicitly as an underlying arithmetic engine for polynomial multiplication within the Binary Ring Learning With Errors(BRLWE)protocol.Tailored for low-power hardware security modules(HSMs)situated at the network edge,the proposed circuit achieves rapid modular multiplication while ensuring a highly compact silicon footprint.By aligning the hardware layout with the precise algebraic properties of the BRLWE variation,this approach delivers a scalable,optimized framework for constructing secure IoT networks capable of resisting quantum adversaries,thereby acting as a pivotal building block for resilient industrial edge protection.Additionally,this study aligns with UN Sustainable Development Goals 8 and 9 by fostering digital trust in emerging technological systems and supporting the safe,adaptive growth of modern electronic economies.展开更多
Post-quantum transport layer security(PQ-TLS)is capable of effectively defending against quantum threats to current network communications,whereas its larger public key and certificate sizes as well as higher computat...Post-quantum transport layer security(PQ-TLS)is capable of effectively defending against quantum threats to current network communications,whereas its larger public key and certificate sizes as well as higher computational overhead may result in a significant performance reduction compared with conventional TLS.In this paper,we present a systematic evaluation of PQ-TLS performance across diverse deployment scenarios to address the following critical research questions.(1)What is the performance behavior of PQ-TLS across different TLS modes?(2)How does PQ-TLS perform across varying client scales?(3)Which network topology is most suitable for PQ-TLS?(4)How does PQ-TLS perform on personal computers(PCs)compared to embedded IoT devices?To the best of our knowledge,this is the first work to comprehensively address these issues,offering implementers some insights into PQ-TLS performance and guidance for optimizing it across diverse scenarios.展开更多
Efficient computation of Tate pairing is a crucial factor for practical applications of pairing-based cryptosystems(PBC).Recently,there have been many improvements for the computation of Tate pairing,which focuses on ...Efficient computation of Tate pairing is a crucial factor for practical applications of pairing-based cryptosystems(PBC).Recently,there have been many improvements for the computation of Tate pairing,which focuses on the arithmetical operations above the finite field.In this paper,we analyze the structure of Miller’s algorithm firstly,which is used to implement Tate pairing.Based on the characteristics that Miller’s algorithm will be improved tremendous if the order of the subgroup of elliptic curve group is low hamming prime,a new method for generating parameters for PBC is put forward,which enable it feasible that there is certain some subgroup of low hamming prime order in the elliptic curve group generated.Finally,we analyze the computation efficiency of Tate pairing using the new parameters for PBC and give the test result.It is clear that the computation of Tate pairing above the elliptic curve group generating by our method can be improved tremendously.展开更多
With the accelerated growth of the Internet of Things(IoT),real-time data processing on edge devices is increasingly important for reducing overhead and enhancing security by keeping sensitive data local.Since these d...With the accelerated growth of the Internet of Things(IoT),real-time data processing on edge devices is increasingly important for reducing overhead and enhancing security by keeping sensitive data local.Since these devices often handle personal information under limited resources,cryptographic algorithms must be executed efficiently.Their computational characteristics strongly affect system performance,making it necessary to analyze resource impact and predict usage under diverse configurations.In this paper,we analyze the phase-level resource usage of AES variants,ChaCha20,ECC,and RSA on an edge device and develop a prediction model.We apply these algorithms under varying parallelism levels and execution strategies across key generation,encryption,and decryption phases.Based on the analysis,we train a unified Random Forest model using execution context and temporal features,achieving R2 values up to 0.994 for power and 0.988 for temperature.Furthermore,the model maintains practical predictive performance even for cryptographic algorithms not included during training,demonstrating its ability to generalize across distinct computational characteristics.Our proposed approach reveals how execution characteristics and resource usage interacts,supporting proactive resource planning and efficient deployment of cryptographic workloads on edge devices.As our approach is grounded in phase-level computational characteristics rather than in any single algorithm,it provides generalizable insights that can be extended to a broader range of cryptographic algorithms that exhibit comparable phase-level execution patterns and to heterogeneous edge architectures.展开更多
Traditional chaotic maps struggle with narrow chaotic ranges and inefficiencies,limiting their use for lightweight,secure image encryption in resource-constrained Wireless Sensor Networks(WSNs).We propose the SPCM,a n...Traditional chaotic maps struggle with narrow chaotic ranges and inefficiencies,limiting their use for lightweight,secure image encryption in resource-constrained Wireless Sensor Networks(WSNs).We propose the SPCM,a novel one-dimensional discontinuous chaotic system integrating polynomial and sine functions,leveraging a piecewise function to achieve a broad chaotic range()and a high Lyapunov exponent(5.04).Validated through nine benchmarks,including standard randomness tests,Diehard tests,and Shannon entropy(3.883),SPCM demonstrates superior randomness and high sensitivity to initial conditions.Applied to image encryption,SPCM achieves 0.152582 s(39%faster than some techniques)and 433.42 KB/s throughput(134%higher than some techniques),setting new benchmarks for chaotic map-based methods in WSNs.Chaos-based permutation and exclusive or(XOR)diffusion yield near-zero correlation in encrypted images,ensuring strong resistance to Statistical Attacks(SA)and accurate recovery.SPCM also exhibits a strong avalanche effect(bit difference),making it an efficient,secure solution for WSNs in domains like healthcare and smart cities.展开更多
As quantum computing continues to advance,traditional cryptographic methods are increasingly challenged,particularly when it comes to securing critical systems like Supervisory Control andData Acquisition(SCADA)system...As quantum computing continues to advance,traditional cryptographic methods are increasingly challenged,particularly when it comes to securing critical systems like Supervisory Control andData Acquisition(SCADA)systems.These systems are essential for monitoring and controlling industrial operations,making their security paramount.A key threat arises from Shor’s algorithm,a powerful quantum computing tool that can compromise current hash functions,leading to significant concerns about data integrity and confidentiality.To tackle these issues,this article introduces a novel Quantum-Resistant Hash Algorithm(QRHA)known as the Modular Hash Learning Algorithm(MHLA).This algorithm is meticulously crafted to withstand potential quantum attacks by incorporating advanced mathematical and algorithmic techniques,enhancing its overall security framework.Our research delves into the effectiveness ofMHLA in defending against both traditional and quantum-based threats,with a particular emphasis on its resilience to Shor’s algorithm.The findings from our study demonstrate that MHLA significantly enhances the security of SCADA systems in the context of quantum technology.By ensuring that sensitive data remains protected and confidential,MHLA not only fortifies individual systems but also contributes to the broader efforts of safeguarding industrial and infrastructure control systems against future quantumthreats.Our evaluation demonstrates that MHLA improves security by 38%against quantumattack simulations compared to traditional hash functionswhilemaintaining a computational efficiency ofO(m⋅n⋅k+v+n).The algorithm achieved a 98%success rate in detecting data tampering during integrity testing.These findings underline MHLA’s effectiveness in enhancing SCADA system security amidst evolving quantum technologies.This research represents a crucial step toward developing more secure cryptographic systems that can adapt to the rapidly changing technological landscape,ultimately ensuring the reliability and integrity of critical infrastructure in an era where quantum computing poses a growing risk.展开更多
Cloud environments are essential for modern computing,but are increasingly vulnerable to Side-Channel Attacks(SCAs),which exploit indirect information to compromise sensitive data.To address this critical challenge,we...Cloud environments are essential for modern computing,but are increasingly vulnerable to Side-Channel Attacks(SCAs),which exploit indirect information to compromise sensitive data.To address this critical challenge,we propose SecureCons Framework(SCF),a novel consensus-based cryptographic framework designed to enhance resilience against SCAs in cloud environments.SCF integrates a dual-layer approach combining lightweight cryptographic algorithms with a blockchain-inspired consensus mechanism to secure data exchanges and thwart potential side-channel exploits.The framework includes adaptive anomaly detection models,cryptographic obfuscation techniques,and real-time monitoring to identify and mitigate vulnerabilities proactively.Experimental evaluations demonstrate the framework's robustness,achieving over 95%resilience against advanced SCAs with minimal computational overhead.SCF provides a scalable,secure,and efficient solution,setting a new benchmark for side-channel attack mitigation in cloud ecosystems.展开更多
The digitization of patient health information has brought many benefits and challenges for both the patients and physicians. However, security and privacy preservation have remained important challenges for remote he...The digitization of patient health information has brought many benefits and challenges for both the patients and physicians. However, security and privacy preservation have remained important challenges for remote health monitoring systems. Since a patient’s health information is sensitive and the communication channel (i.e. the Internet) is insecure, it is important to protect them against unauthorized entities. Otherwise, failure to do so will not only lead to compromise of a patient’s privacy, but will also put his/her life at risk. How to provide for confidentiality, patient anonymity and un-traceability, access control to a patient’s health information and even key exchange between a patient and her physician are critical issues that need to be addressed if a wider adoption of remote health monitoring systems is to be realized. This paper proposes an authenticated privacy preserving pairing-based scheme for remote health monitoring systems. The scheme is based on the concepts of bilinear paring, identity-based cryptography and non-interactive identity-based key agreement protocol. The scheme also incorporates an efficient batch signature verification scheme to reduce computation cost during multiple simultaneous signature verifications.展开更多
Smart cities,as a typical application in the field of the Internet of Things,can combine cloud computing to realize the intelligent control of objects and process massive data.While cloud computing brings convenience ...Smart cities,as a typical application in the field of the Internet of Things,can combine cloud computing to realize the intelligent control of objects and process massive data.While cloud computing brings convenience to smart city services,a serious problem is ensuring that confidential data cannot be leaked to malicious adversaries.Considering the security and privacy of data,data owners transmit sensitive data in its encrypted form to cloud server,which seriously hinders the improvements of potential utilization and efficient sharing.Public key searchable encryption ensures that users can securely retrieve the encrypted data without decryption.However,most existing schemes cannot resist keyword guessing attacks or the size of trapdoors linearly increases with the number of data owners.In this work,by utilizing certificateless encryption and proxy re-encryption,we design an authenticated searchable encryption scheme with constant trapdoors.The designed scheme preserves the privacy of index ciphertexts and keyword trapdoors,and can resist keyword guessing attacks.In addition,data users can generate and upload trapdoors with lower computation and communication overheads.We show that the proposed scheme is suitable for smart city implementations and applications by experimentally evaluating its performance.展开更多
Adversarial jamming attacks have increased on communication systems,causing distortion and threatening transmissions.Typical attacks rely on traditional,well-defined cryptographic protocols and frequency-hopping techn...Adversarial jamming attacks have increased on communication systems,causing distortion and threatening transmissions.Typical attacks rely on traditional,well-defined cryptographic protocols and frequency-hopping techniques.Nevertheless,these techniques become vulnerable when facing intelligent jammers.To address this issue,we introduce a new framework that integrates Siamese neural networks with a dual-probability-attention mechanism(DPAM)to provide reliable anti-jamming communication and robust protection.This framework contains several components,which are(1)twin neural networks to execute coordinated cryptographic adaptation operation using a contrastive learning approach,(2)a DPAM module to analyse signals using probability encoding and dual temporal-spectral attention to enhance accurate recognition,(3)adversarial training to counter growing attack patterns and(4)a lightweight neural encryption module that is developed to provide real-time operation.Internal DPAM architecture combines probability distributions with Bayesian attention fusion.This combination increases the detection by 23%when compared to other attention mechanisms.Conducted simulation evaluations on a public dataset shows that the frameworks reached an accuracy of 98.7%,whereas other reinforcement learning(RL)methods achieved 82%.In addition,45%reduction in latency was reached when compared to frequency-hopping solutions.Furthermore,the solution got up to 96%resilience against attacks.展开更多
基金funded by Prince Sattam bin Abdulaziz University,grant number PSAU/2025/01/34935.
摘要The ongoing expansion of the Internet of Things(IoT)fundamentally alters industrial and economic paradigms by integrating intelligent nodes throughout operational frameworks.Nonetheless,vulnerabilities surrounding system integrity and data confidentiality present major bottlenecks to widespread adoption,a dilemma severely intensified by impending quantum computing capabilities.Defending these networks demands the integration of post-quantum cryptographic primitives;yet,the severe hardware constraints characterizing peripheral IoT components complicate practical deployment.Quantum-resistant lattice cryptography offers a highly promising pathway to overcome these limitations,largely because the foundational security and throughput of these protocols hinge on polynomial multiplication performance.Consequently,optimizing the computational speed and architectural efficiency of this specific algebraic operation drastically enhances the viability of lattice-reliant defense mechanisms.To address this need,this study develops a specialized systolic array architecture engineered explicitly as an underlying arithmetic engine for polynomial multiplication within the Binary Ring Learning With Errors(BRLWE)protocol.Tailored for low-power hardware security modules(HSMs)situated at the network edge,the proposed circuit achieves rapid modular multiplication while ensuring a highly compact silicon footprint.By aligning the hardware layout with the precise algebraic properties of the BRLWE variation,this approach delivers a scalable,optimized framework for constructing secure IoT networks capable of resisting quantum adversaries,thereby acting as a pivotal building block for resilient industrial edge protection.Additionally,this study aligns with UN Sustainable Development Goals 8 and 9 by fostering digital trust in emerging technological systems and supporting the safe,adaptive growth of modern electronic economies.
基金Special Fund for Key Technologies in Blockchain of Shanghai Scientific and Technological Committee(23511100300)。
摘要Post-quantum transport layer security(PQ-TLS)is capable of effectively defending against quantum threats to current network communications,whereas its larger public key and certificate sizes as well as higher computational overhead may result in a significant performance reduction compared with conventional TLS.In this paper,we present a systematic evaluation of PQ-TLS performance across diverse deployment scenarios to address the following critical research questions.(1)What is the performance behavior of PQ-TLS across different TLS modes?(2)How does PQ-TLS perform across varying client scales?(3)Which network topology is most suitable for PQ-TLS?(4)How does PQ-TLS perform on personal computers(PCs)compared to embedded IoT devices?To the best of our knowledge,this is the first work to comprehensively address these issues,offering implementers some insights into PQ-TLS performance and guidance for optimizing it across diverse scenarios.
基金supported by National Nature Science Foundation of China under Grant No.60873107 to G.M.Dai,Nature Science Foundation CD2008438B to G.M.Daiin Hubei under Grant No.Special Funds to Finance Operating Expenses for Basic Scientific Research of Central Colleges in China under Grant No.CUGL090241 to M.C.Wang.
摘要Efficient computation of Tate pairing is a crucial factor for practical applications of pairing-based cryptosystems(PBC).Recently,there have been many improvements for the computation of Tate pairing,which focuses on the arithmetical operations above the finite field.In this paper,we analyze the structure of Miller’s algorithm firstly,which is used to implement Tate pairing.Based on the characteristics that Miller’s algorithm will be improved tremendous if the order of the subgroup of elliptic curve group is low hamming prime,a new method for generating parameters for PBC is put forward,which enable it feasible that there is certain some subgroup of low hamming prime order in the elliptic curve group generated.Finally,we analyze the computation efficiency of Tate pairing using the new parameters for PBC and give the test result.It is clear that the computation of Tate pairing above the elliptic curve group generating by our method can be improved tremendously.
基金supported in part by the National Research Foundation of Korea(NRF)(No.RS-2025-00554650)supported by the Chung-Ang University research grant in 2024。
摘要With the accelerated growth of the Internet of Things(IoT),real-time data processing on edge devices is increasingly important for reducing overhead and enhancing security by keeping sensitive data local.Since these devices often handle personal information under limited resources,cryptographic algorithms must be executed efficiently.Their computational characteristics strongly affect system performance,making it necessary to analyze resource impact and predict usage under diverse configurations.In this paper,we analyze the phase-level resource usage of AES variants,ChaCha20,ECC,and RSA on an edge device and develop a prediction model.We apply these algorithms under varying parallelism levels and execution strategies across key generation,encryption,and decryption phases.Based on the analysis,we train a unified Random Forest model using execution context and temporal features,achieving R2 values up to 0.994 for power and 0.988 for temperature.Furthermore,the model maintains practical predictive performance even for cryptographic algorithms not included during training,demonstrating its ability to generalize across distinct computational characteristics.Our proposed approach reveals how execution characteristics and resource usage interacts,supporting proactive resource planning and efficient deployment of cryptographic workloads on edge devices.As our approach is grounded in phase-level computational characteristics rather than in any single algorithm,it provides generalizable insights that can be extended to a broader range of cryptographic algorithms that exhibit comparable phase-level execution patterns and to heterogeneous edge architectures.
基金supported by the National Research Foundation of Korea(NRF)grant funded by the Korean government Ministry of Science and ICT(MIST)(RS-2022-00165225).
摘要Traditional chaotic maps struggle with narrow chaotic ranges and inefficiencies,limiting their use for lightweight,secure image encryption in resource-constrained Wireless Sensor Networks(WSNs).We propose the SPCM,a novel one-dimensional discontinuous chaotic system integrating polynomial and sine functions,leveraging a piecewise function to achieve a broad chaotic range()and a high Lyapunov exponent(5.04).Validated through nine benchmarks,including standard randomness tests,Diehard tests,and Shannon entropy(3.883),SPCM demonstrates superior randomness and high sensitivity to initial conditions.Applied to image encryption,SPCM achieves 0.152582 s(39%faster than some techniques)and 433.42 KB/s throughput(134%higher than some techniques),setting new benchmarks for chaotic map-based methods in WSNs.Chaos-based permutation and exclusive or(XOR)diffusion yield near-zero correlation in encrypted images,ensuring strong resistance to Statistical Attacks(SA)and accurate recovery.SPCM also exhibits a strong avalanche effect(bit difference),making it an efficient,secure solution for WSNs in domains like healthcare and smart cities.
基金Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R343),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabiathe Deanship of Scientific Research at Northern Border University,Arar,Saudi Arabia for funding this research work through the project number NBU-FFR-2025-1092-10.
摘要As quantum computing continues to advance,traditional cryptographic methods are increasingly challenged,particularly when it comes to securing critical systems like Supervisory Control andData Acquisition(SCADA)systems.These systems are essential for monitoring and controlling industrial operations,making their security paramount.A key threat arises from Shor’s algorithm,a powerful quantum computing tool that can compromise current hash functions,leading to significant concerns about data integrity and confidentiality.To tackle these issues,this article introduces a novel Quantum-Resistant Hash Algorithm(QRHA)known as the Modular Hash Learning Algorithm(MHLA).This algorithm is meticulously crafted to withstand potential quantum attacks by incorporating advanced mathematical and algorithmic techniques,enhancing its overall security framework.Our research delves into the effectiveness ofMHLA in defending against both traditional and quantum-based threats,with a particular emphasis on its resilience to Shor’s algorithm.The findings from our study demonstrate that MHLA significantly enhances the security of SCADA systems in the context of quantum technology.By ensuring that sensitive data remains protected and confidential,MHLA not only fortifies individual systems but also contributes to the broader efforts of safeguarding industrial and infrastructure control systems against future quantumthreats.Our evaluation demonstrates that MHLA improves security by 38%against quantumattack simulations compared to traditional hash functionswhilemaintaining a computational efficiency ofO(m⋅n⋅k+v+n).The algorithm achieved a 98%success rate in detecting data tampering during integrity testing.These findings underline MHLA’s effectiveness in enhancing SCADA system security amidst evolving quantum technologies.This research represents a crucial step toward developing more secure cryptographic systems that can adapt to the rapidly changing technological landscape,ultimately ensuring the reliability and integrity of critical infrastructure in an era where quantum computing poses a growing risk.
摘要Cloud environments are essential for modern computing,but are increasingly vulnerable to Side-Channel Attacks(SCAs),which exploit indirect information to compromise sensitive data.To address this critical challenge,we propose SecureCons Framework(SCF),a novel consensus-based cryptographic framework designed to enhance resilience against SCAs in cloud environments.SCF integrates a dual-layer approach combining lightweight cryptographic algorithms with a blockchain-inspired consensus mechanism to secure data exchanges and thwart potential side-channel exploits.The framework includes adaptive anomaly detection models,cryptographic obfuscation techniques,and real-time monitoring to identify and mitigate vulnerabilities proactively.Experimental evaluations demonstrate the framework's robustness,achieving over 95%resilience against advanced SCAs with minimal computational overhead.SCF provides a scalable,secure,and efficient solution,setting a new benchmark for side-channel attack mitigation in cloud ecosystems.
摘要The digitization of patient health information has brought many benefits and challenges for both the patients and physicians. However, security and privacy preservation have remained important challenges for remote health monitoring systems. Since a patient’s health information is sensitive and the communication channel (i.e. the Internet) is insecure, it is important to protect them against unauthorized entities. Otherwise, failure to do so will not only lead to compromise of a patient’s privacy, but will also put his/her life at risk. How to provide for confidentiality, patient anonymity and un-traceability, access control to a patient’s health information and even key exchange between a patient and her physician are critical issues that need to be addressed if a wider adoption of remote health monitoring systems is to be realized. This paper proposes an authenticated privacy preserving pairing-based scheme for remote health monitoring systems. The scheme is based on the concepts of bilinear paring, identity-based cryptography and non-interactive identity-based key agreement protocol. The scheme also incorporates an efficient batch signature verification scheme to reduce computation cost during multiple simultaneous signature verifications.
基金supported by the Shandong Provincial Key Research and Development Program(No.2021CXGC010107)the National Natural Science Foundation of China(Nos.U21A20466,62325209)+3 种基金the New 20 Project of Higher Education of Jinan(No.202228017)the Special Project on Science and Technology Program of Hubei Province(No.2021BAA025)the Fundamental Research Funds for the Central Universities(Nos.2042023kf0203,20420241013)the Researchers Supporting Project Number(RSP2024R509),King Saud University,Riyadh,Saudi Arabia。
摘要Smart cities,as a typical application in the field of the Internet of Things,can combine cloud computing to realize the intelligent control of objects and process massive data.While cloud computing brings convenience to smart city services,a serious problem is ensuring that confidential data cannot be leaked to malicious adversaries.Considering the security and privacy of data,data owners transmit sensitive data in its encrypted form to cloud server,which seriously hinders the improvements of potential utilization and efficient sharing.Public key searchable encryption ensures that users can securely retrieve the encrypted data without decryption.However,most existing schemes cannot resist keyword guessing attacks or the size of trapdoors linearly increases with the number of data owners.In this work,by utilizing certificateless encryption and proxy re-encryption,we design an authenticated searchable encryption scheme with constant trapdoors.The designed scheme preserves the privacy of index ciphertexts and keyword trapdoors,and can resist keyword guessing attacks.In addition,data users can generate and upload trapdoors with lower computation and communication overheads.We show that the proposed scheme is suitable for smart city implementations and applications by experimentally evaluating its performance.
基金funded by the Deanship of Scientific Research (DSR) at King Abdulaziz University,Jeddah,Saudi Arabia under Grant (IPP:1489-829-2025)DSR for technical and financial support.
摘要Adversarial jamming attacks have increased on communication systems,causing distortion and threatening transmissions.Typical attacks rely on traditional,well-defined cryptographic protocols and frequency-hopping techniques.Nevertheless,these techniques become vulnerable when facing intelligent jammers.To address this issue,we introduce a new framework that integrates Siamese neural networks with a dual-probability-attention mechanism(DPAM)to provide reliable anti-jamming communication and robust protection.This framework contains several components,which are(1)twin neural networks to execute coordinated cryptographic adaptation operation using a contrastive learning approach,(2)a DPAM module to analyse signals using probability encoding and dual temporal-spectral attention to enhance accurate recognition,(3)adversarial training to counter growing attack patterns and(4)a lightweight neural encryption module that is developed to provide real-time operation.Internal DPAM architecture combines probability distributions with Bayesian attention fusion.This combination increases the detection by 23%when compared to other attention mechanisms.Conducted simulation evaluations on a public dataset shows that the frameworks reached an accuracy of 98.7%,whereas other reinforcement learning(RL)methods achieved 82%.In addition,45%reduction in latency was reached when compared to frequency-hopping solutions.Furthermore,the solution got up to 96%resilience against attacks.