Phishing is a type of cybercrime in which cyber-attackers pose themselves as authorized persons or entities and hack the victims’sensitive data.E-mails,instant messages and phone calls are some of the common modes us...Phishing is a type of cybercrime in which cyber-attackers pose themselves as authorized persons or entities and hack the victims’sensitive data.E-mails,instant messages and phone calls are some of the common modes used in cyberattacks.Though the security models are continuously upgraded to prevent cyberattacks,hackers find innovative ways to target the victims.In this background,there is a drastic increase observed in the number of phishing emails sent to potential targets.This scenario necessitates the importance of designing an effective classification model.Though numerous conventional models are available in the literature for proficient classification of phishing emails,the Machine Learning(ML)techniques and the Deep Learning(DL)models have been employed in the literature.The current study presents an Intelligent Cuckoo Search(CS)Optimization Algorithm with a Deep Learning-based Phishing Email Detection and Classification(ICSOA-DLPEC)model.The aim of the proposed ICSOA-DLPEC model is to effectually distinguish the emails as either legitimate or phishing ones.At the initial stage,the pre-processing is performed through three stages such as email cleaning,tokenization and stop-word elimination.Then,the N-gram approach is;moreover,the CS algorithm is applied to extract the useful feature vectors.Moreover,the CS algorithm is employed with the Gated Recurrent Unit(GRU)model to detect and classify phishing emails.Furthermore,the CS algorithm is used to fine-tune the parameters involved in the GRU model.The performance of the proposed ICSOA-DLPEC model was experimentally validated using a benchmark dataset,and the results were assessed under several dimensions.Extensive comparative studies were conducted,and the results confirmed the superior performance of the proposed ICSOA-DLPEC model over other existing approaches.The proposed model achieved a maximum accuracy of 99.72%.展开更多
This works intends to provide numerical solutions based on the nonlinear fractional order derivatives of the classical White and Comiskey model(NFD-WCM).The fractional order derivatives have provided authentic and acc...This works intends to provide numerical solutions based on the nonlinear fractional order derivatives of the classical White and Comiskey model(NFD-WCM).The fractional order derivatives have provided authentic and accurate solutions for the NDF-WCM.The solutions of the fractional NFD-WCM are provided using the stochastic computing supervised algorithm named Levenberg-Marquard Backpropagation(LMB)based on neural networks(NNs).This regression approach combines gradient descent and Gauss-Newton iterative methods,which means finding a solution through the sequences of different calculations.WCM is used to demonstrate the heroin epidemics.Heroin has been on-growth world wide,mainly in Asia,Europe,and the USA.It is the fourth foremost cause of death due to taking an overdose in the USA.The nonlinear mathematical system NFD-WCM discusses the overall circumstance of different drug users,such as suspected groups,drug users without treatment,and drug users with treatment.The numerical results of NFD-WCM via LMB-NNs have been substantiated through training,testing,and validation measures.The stability and accuracy are then checked through the statistical tool,such asmean square error(MSE),error histogram,and fitness curves.The suggested methodology’s strength is demonstrated by the high convergence between the reference solutions and the solutions generated by adding the efficacy of a constructed solver LMB-NNs,with accuracy levels ranging from 10?9 to 10?10.展开更多
The Mobile Ad-hoc Network(MANET)is a dynamic topology that provides a variety of executions in various disciplines.The most sticky topic in organizationalfields was MANET protection.MANET is helpless against various t...The Mobile Ad-hoc Network(MANET)is a dynamic topology that provides a variety of executions in various disciplines.The most sticky topic in organizationalfields was MANET protection.MANET is helpless against various threats that affect its usability and accessibility.The dark opening assault is considered one of the most far-reaching dynamic assaults that deteriorate the organi-zation's execution and reliability by dropping all approaching packages via the noxious node.The Dark Opening Node aims to deceive any node in the company that wishes to connect to another node by pretending to get the most delicate ability to support the target node.Ad-hoc On-demand Distance Vector(AODV)is a responsive steering convention with no corporate techniques to locate and destroy the dark opening center.We improved AODV by incorporating a novel compact method for detecting and isolating lonely and collaborative black-hole threats that utilize clocks and baits.The recommended method allows MANET nodes to discover and segregate black-hole network nodes over dynamic changes in the network topology.We implement the suggested method's performance with the help of Network Simulator(NS)-3 simulation models.Furthermore,the proposed approach comes exceptionally near to the original AODV,absent black holes in terms of bandwidth,end-to-end latency,error rate,and delivery ratio.展开更多
The Internet of Vehicles(IoV)is an emerging technology that aims to connect vehicles,infrastructure,and other devices to enable intelligent transportation systems.One of the key challenges in IoV is to ensure safe and...The Internet of Vehicles(IoV)is an emerging technology that aims to connect vehicles,infrastructure,and other devices to enable intelligent transportation systems.One of the key challenges in IoV is to ensure safe and efficient communication among vehicles of different types and capabilities.This paper proposes a data-driven vehicular heterogeneity-based intelligent collision avoidance system for IoV.The system leverages Vehicle-to-Vehicle(V2V)and Vehicle-to-Infrastructure(V2I)communication to collect real-time data about the environment and the vehicles.The data is collected to acknowledge the heterogeneity of vehicles and human behavior.The data is analyzed using machine learning algorithms to identify potential collision risks and recommend appropriate actions to avoid collisions.The system takes into account the heterogeneity of vehicles,such as their size,speed,and maneuverability,to optimize collision avoidance strategies.The proposed system is experimented with real-time datasets and compared with existing collision avoidance systems.The results are shown using the evaluation metrics that show the proposed system can significantly reduce the number of collisions and improve the overall safety and efficiency of IoV with an accuracy of 96.5%using the SVM algorithm.The trial outcomes demonstrated that the new system,incorporating vehicular,weather,and human behavior factors,outperformed previous systems that only considered vehicular and weather aspects.This innovative approach is poised to lead transportation efforts,reducing accident rates and improving the quality of transportation systems in smart cities.By offering predictive capabilities,the proposed model not only helps control accident rates but also prevents them in advance,ensuring road safety.展开更多
Smart City Healthcare(SHC2)system is applied in monitoring the patient at home while it is also expected to react to their needs in a timely manner.The system also concedes the freedom of a patient.IoT is a part of th...Smart City Healthcare(SHC2)system is applied in monitoring the patient at home while it is also expected to react to their needs in a timely manner.The system also concedes the freedom of a patient.IoT is a part of this system and it helps in providing care to the patients.IoTbased healthcare devices are trustworthy since it almost certainly recognizes the potential intensifications at very early stage and alerts the patients and medical experts to such an extent that they are provided with immediate care.Existing methodologies exhibit few shortcomings in terms of computational complexity,cost and data security.Hence,the current research article examines SHC2 security through LightWeight Cipher(LWC)with Optimal S-Box model in PRESENT cipher.This procedure aims at changing the sub bytes in which a single function is connected with several bytes’information to upgrade the security level through Swam optimization.The key contribution of this research article is the development of a secure healthcare model for smart city using SHC2 security via LWC and Optimal S-Box models.The study used a nonlinear layer and single 4-bit S box for round configuration after verifying SHC2 information,constrained by Mutual Authentication(MA).The security challenges,in healthcare information systems,emphasize the need for a methodology that immovably concretes the establishments.The methodology should act practically,be an effective healthcare framework that depends on solidarity and adapts to the developing threats.Healthcare service providers integrated the IoT applications and medical services to offer individuals,a seamless technology-supported healthcare service.The proposed SHC^(2) was implemented to demonstrate its security levels in terms of time and access policies.The model was tested under different parameters such as encryption time,decryption time,access time and response time inminimum range.Then,the level of the model and throughput were analyzed by maximum value i.e.,50Mbps/sec and 95.56%for PRESENT-Authorization cipher to achieve smart city security.The proposed model achieved better results than the existing methodologies.展开更多
This study proposes a fault detection algorithm based on the analysis of the theoretical curves which describe the behaviour of an existing grid-connected photovoltaic(GCPV)system.For a given set of working conditions...This study proposes a fault detection algorithm based on the analysis of the theoretical curves which describe the behaviour of an existing grid-connected photovoltaic(GCPV)system.For a given set of working conditions,a number of attributes such as voltage ratio(VR)and power ratio(PR)are simulated using virtual instrumentation LabVIEW software.Furthermore,a third-order polynomial function is used to generate two detection limits(high and low limits)for the VR and PR ratios.The high and low detection limits are compared with real-time long-term data measurements from a 1.1 kWp GCPV system installed at the University of Huddersfield,United Kingdom.Furthermore,samples that lie out of the detecting limits are processed by a fuzzy logic classification system which consists of two inputs(VR and PR)and one output membership function.The obtained results show that the fault detection algorithm accurately detects different faults occurring in the PV system.The maximum detection accuracy(DA)of the proposed algorithm before considering the fuzzy logic system is equal to 95.27%;however,the fault DA is increased up to a minimum value of 98.8%after considering the fuzzy logic system.展开更多
基金This research was supported in part by Basic Science Research Program through the National Research Foundation of Korea(NRF),funded by the Ministry of Education(NRF-2021R1A6A1A03039493)in part by the NRF grant funded by the Korea government(MSIT)(NRF-2022R1A2C1004401).
文摘Phishing is a type of cybercrime in which cyber-attackers pose themselves as authorized persons or entities and hack the victims’sensitive data.E-mails,instant messages and phone calls are some of the common modes used in cyberattacks.Though the security models are continuously upgraded to prevent cyberattacks,hackers find innovative ways to target the victims.In this background,there is a drastic increase observed in the number of phishing emails sent to potential targets.This scenario necessitates the importance of designing an effective classification model.Though numerous conventional models are available in the literature for proficient classification of phishing emails,the Machine Learning(ML)techniques and the Deep Learning(DL)models have been employed in the literature.The current study presents an Intelligent Cuckoo Search(CS)Optimization Algorithm with a Deep Learning-based Phishing Email Detection and Classification(ICSOA-DLPEC)model.The aim of the proposed ICSOA-DLPEC model is to effectually distinguish the emails as either legitimate or phishing ones.At the initial stage,the pre-processing is performed through three stages such as email cleaning,tokenization and stop-word elimination.Then,the N-gram approach is;moreover,the CS algorithm is applied to extract the useful feature vectors.Moreover,the CS algorithm is employed with the Gated Recurrent Unit(GRU)model to detect and classify phishing emails.Furthermore,the CS algorithm is used to fine-tune the parameters involved in the GRU model.The performance of the proposed ICSOA-DLPEC model was experimentally validated using a benchmark dataset,and the results were assessed under several dimensions.Extensive comparative studies were conducted,and the results confirmed the superior performance of the proposed ICSOA-DLPEC model over other existing approaches.The proposed model achieved a maximum accuracy of 99.72%.
基金National Research Council of Thailand(NRCT)and Khon Kaen University:N42A650291.
文摘This works intends to provide numerical solutions based on the nonlinear fractional order derivatives of the classical White and Comiskey model(NFD-WCM).The fractional order derivatives have provided authentic and accurate solutions for the NDF-WCM.The solutions of the fractional NFD-WCM are provided using the stochastic computing supervised algorithm named Levenberg-Marquard Backpropagation(LMB)based on neural networks(NNs).This regression approach combines gradient descent and Gauss-Newton iterative methods,which means finding a solution through the sequences of different calculations.WCM is used to demonstrate the heroin epidemics.Heroin has been on-growth world wide,mainly in Asia,Europe,and the USA.It is the fourth foremost cause of death due to taking an overdose in the USA.The nonlinear mathematical system NFD-WCM discusses the overall circumstance of different drug users,such as suspected groups,drug users without treatment,and drug users with treatment.The numerical results of NFD-WCM via LMB-NNs have been substantiated through training,testing,and validation measures.The stability and accuracy are then checked through the statistical tool,such asmean square error(MSE),error histogram,and fitness curves.The suggested methodology’s strength is demonstrated by the high convergence between the reference solutions and the solutions generated by adding the efficacy of a constructed solver LMB-NNs,with accuracy levels ranging from 10?9 to 10?10.
文摘The Mobile Ad-hoc Network(MANET)is a dynamic topology that provides a variety of executions in various disciplines.The most sticky topic in organizationalfields was MANET protection.MANET is helpless against various threats that affect its usability and accessibility.The dark opening assault is considered one of the most far-reaching dynamic assaults that deteriorate the organi-zation's execution and reliability by dropping all approaching packages via the noxious node.The Dark Opening Node aims to deceive any node in the company that wishes to connect to another node by pretending to get the most delicate ability to support the target node.Ad-hoc On-demand Distance Vector(AODV)is a responsive steering convention with no corporate techniques to locate and destroy the dark opening center.We improved AODV by incorporating a novel compact method for detecting and isolating lonely and collaborative black-hole threats that utilize clocks and baits.The recommended method allows MANET nodes to discover and segregate black-hole network nodes over dynamic changes in the network topology.We implement the suggested method's performance with the help of Network Simulator(NS)-3 simulation models.Furthermore,the proposed approach comes exceptionally near to the original AODV,absent black holes in terms of bandwidth,end-to-end latency,error rate,and delivery ratio.
文摘The Internet of Vehicles(IoV)is an emerging technology that aims to connect vehicles,infrastructure,and other devices to enable intelligent transportation systems.One of the key challenges in IoV is to ensure safe and efficient communication among vehicles of different types and capabilities.This paper proposes a data-driven vehicular heterogeneity-based intelligent collision avoidance system for IoV.The system leverages Vehicle-to-Vehicle(V2V)and Vehicle-to-Infrastructure(V2I)communication to collect real-time data about the environment and the vehicles.The data is collected to acknowledge the heterogeneity of vehicles and human behavior.The data is analyzed using machine learning algorithms to identify potential collision risks and recommend appropriate actions to avoid collisions.The system takes into account the heterogeneity of vehicles,such as their size,speed,and maneuverability,to optimize collision avoidance strategies.The proposed system is experimented with real-time datasets and compared with existing collision avoidance systems.The results are shown using the evaluation metrics that show the proposed system can significantly reduce the number of collisions and improve the overall safety and efficiency of IoV with an accuracy of 96.5%using the SVM algorithm.The trial outcomes demonstrated that the new system,incorporating vehicular,weather,and human behavior factors,outperformed previous systems that only considered vehicular and weather aspects.This innovative approach is poised to lead transportation efforts,reducing accident rates and improving the quality of transportation systems in smart cities.By offering predictive capabilities,the proposed model not only helps control accident rates but also prevents them in advance,ensuring road safety.
文摘Smart City Healthcare(SHC2)system is applied in monitoring the patient at home while it is also expected to react to their needs in a timely manner.The system also concedes the freedom of a patient.IoT is a part of this system and it helps in providing care to the patients.IoTbased healthcare devices are trustworthy since it almost certainly recognizes the potential intensifications at very early stage and alerts the patients and medical experts to such an extent that they are provided with immediate care.Existing methodologies exhibit few shortcomings in terms of computational complexity,cost and data security.Hence,the current research article examines SHC2 security through LightWeight Cipher(LWC)with Optimal S-Box model in PRESENT cipher.This procedure aims at changing the sub bytes in which a single function is connected with several bytes’information to upgrade the security level through Swam optimization.The key contribution of this research article is the development of a secure healthcare model for smart city using SHC2 security via LWC and Optimal S-Box models.The study used a nonlinear layer and single 4-bit S box for round configuration after verifying SHC2 information,constrained by Mutual Authentication(MA).The security challenges,in healthcare information systems,emphasize the need for a methodology that immovably concretes the establishments.The methodology should act practically,be an effective healthcare framework that depends on solidarity and adapts to the developing threats.Healthcare service providers integrated the IoT applications and medical services to offer individuals,a seamless technology-supported healthcare service.The proposed SHC^(2) was implemented to demonstrate its security levels in terms of time and access policies.The model was tested under different parameters such as encryption time,decryption time,access time and response time inminimum range.Then,the level of the model and throughput were analyzed by maximum value i.e.,50Mbps/sec and 95.56%for PRESENT-Authorization cipher to achieve smart city security.The proposed model achieved better results than the existing methodologies.
文摘This study proposes a fault detection algorithm based on the analysis of the theoretical curves which describe the behaviour of an existing grid-connected photovoltaic(GCPV)system.For a given set of working conditions,a number of attributes such as voltage ratio(VR)and power ratio(PR)are simulated using virtual instrumentation LabVIEW software.Furthermore,a third-order polynomial function is used to generate two detection limits(high and low limits)for the VR and PR ratios.The high and low detection limits are compared with real-time long-term data measurements from a 1.1 kWp GCPV system installed at the University of Huddersfield,United Kingdom.Furthermore,samples that lie out of the detecting limits are processed by a fuzzy logic classification system which consists of two inputs(VR and PR)and one output membership function.The obtained results show that the fault detection algorithm accurately detects different faults occurring in the PV system.The maximum detection accuracy(DA)of the proposed algorithm before considering the fuzzy logic system is equal to 95.27%;however,the fault DA is increased up to a minimum value of 98.8%after considering the fuzzy logic system.
