A combination of nucleos(t)ides and hepatitis B immunoglobulin(HBIg)has been found to be effective for the prevention of hepatitis B viral(HBV)reinfection after liver transplantation(LT),but its administration is cost...A combination of nucleos(t)ides and hepatitis B immunoglobulin(HBIg)has been found to be effective for the prevention of hepatitis B viral(HBV)reinfection after liver transplantation(LT),but its administration is costly,and not always available.We report the case of a male,33-year-old cirrhotic patient who has tested positive for serum HBsAg,and HBeAg,with 9.04×107 copies/mL of HBV DNA.He suffered from acute liver failure and was near death before undergoing emergency LT.No HBIg was available at the time,so only lamivudine was used.He routinely received immunosuppression medication.Serum HBV DNA and HBsAg still showed positive post-LT,and the graft re-infected.Hepatitis B flared three months later.Adefovir dipivoxil was added to the treatment,but in the 24th mo of treatment,the patient developed lamivudine resistance and a worsening of the hepatitis occurred shortly thereafter.The treatment combination was then changed to a double dosage of entecavir and the disease was gradually resolved.After 60-mo of post-LT nucleos(t)ide analogue therapy,anti-HBs seroconverted,and the antiviral was stopped.By the end of a 12-mo follow-up,the patient had achieved sustained recovery.In conclusion,the case seems to point to evidence that more potent and less resistant analogues like entecavir might fully replace HBIg as an HBV prophylaxis and treatment regimen.展开更多
In this paper,we used higher order Haar wavelet method(HOHWM),introduced by Majak et al.[1],for approximate solution of second order integro-diferential equations(IDEs)of second-kind.It is improvement of long-establis...In this paper,we used higher order Haar wavelet method(HOHWM),introduced by Majak et al.[1],for approximate solution of second order integro-diferential equations(IDEs)of second-kind.It is improvement of long-established Haar wavelet collocation method(HWCM)which has been much popular among researchers and has many applications in literature.Present study aims to improve the numerical results of second order IDEs from first order rate of convergence in case of HWCM to the second and fourth order rate of convergence using HOHWM,depending on parameterλfor values 1 and 2,respectively.Several problems available in the literature of both,Volterra and Fredholm type of IDEs,are tested and compared with HWCM to illustrate the performance of our proposed method.展开更多
Android smartphones have become an integral part of our daily lives,becoming targets for ransomware attacks.Such attacks encrypt user information and ask for payment to recover it.Conventional detection mechanisms,suc...Android smartphones have become an integral part of our daily lives,becoming targets for ransomware attacks.Such attacks encrypt user information and ask for payment to recover it.Conventional detection mechanisms,such as signature-based and heuristic techniques,often fail to detect new and polymorphic ransomware samples.To address this challenge,we employed various ensemble classifiers,such as Random Forest,Gradient Boosting,Bagging,and AutoML models.We aimed to showcase how AutoML can automate processes such as model selection,feature engineering,and hyperparameter optimization,to minimize manual effort while ensuring or enhancing performance compared to traditional approaches.We used this framework to test it with a publicly available dataset from the Kaggle repository,which contains features for Android ransomware network traffic.The dataset comprises 392,024 flow records,divided into eleven groups.There are ten classes for various ransomware types,including SVpeng,PornDroid,Koler,WannaLocker,and Lockerpin.There is also a class for regular traffic.We applied a three-step procedure to select themost relevant features:filter,wrapper,and embeddedmethods.The Bagging classifier was highly accurate,correctly getting 99.84%of the time.The FLAML AutoML framework was evenmore accurate,correctly getting 99.85%of the time.This is indicative of howwellAutoML performs in improving things with minimal human assistance.Our findings indicate that AutoML is an efficient,scalable,and flexible method to discover Android ransomware,and it will facilitate the development of next-generation intrusion detection systems.展开更多
The rapid growth of IoT networks necessitates efficient Intrusion Detection Systems(IDS)capable of addressing dynamic security threats under constrained resource environments.This paper proposes a hybrid IDS for IoT n...The rapid growth of IoT networks necessitates efficient Intrusion Detection Systems(IDS)capable of addressing dynamic security threats under constrained resource environments.This paper proposes a hybrid IDS for IoT networks,integrating Support Vector Machine(SVM)and Genetic Algorithm(GA)for feature selection and parameter optimization.The GA reduces the feature set from 41 to 7,achieving a 30%reduction in overhead while maintaining an attack detection rate of 98.79%.Evaluated on the NSL-KDD dataset,the system demonstrates an accuracy of 97.36%,a recall of 98.42%,and an F1-score of 96.67%,with a low false positive rate of 1.5%.Additionally,it effectively detects critical User-to-Root(U2R)attacks at a rate of 96.2%and Remote-to-Local(R2L)attacks at 95.8%.Performance tests validate the system’s scalability for networks with up to 2000 nodes,with detection latencies of 120 ms at 65%CPU utilization in small-scale deployments and 250 ms at 85%CPU utilization in large-scale scenarios.Parameter sensitivity analysis enhances model robustness,while false positive examination aids in reducing administrative overhead for practical deployment.This IDS offers an effective,scalable,and resource-efficient solution for real-world IoT system security,outperforming traditional approaches.展开更多
The exponential growth of the Internet of Things(IoT)has introduced significant security challenges,with zero-day attacks emerging as one of the most critical and challenging threats.Traditional Machine Learning(ML)an...The exponential growth of the Internet of Things(IoT)has introduced significant security challenges,with zero-day attacks emerging as one of the most critical and challenging threats.Traditional Machine Learning(ML)and Deep Learning(DL)techniques have demonstrated promising early detection capabilities.However,their effectiveness is limited when handling the vast volumes of IoT-generated data due to scalability constraints,high computational costs,and the costly time-intensive process of data labeling.To address these challenges,this study proposes a Federated Learning(FL)framework that leverages collaborative and hybrid supervised learning to enhance cyber threat detection in IoT networks.By employing Deep Neural Networks(DNNs)and decentralized model training,the approach reduces computational complexity while improving detection accuracy.The proposed model demonstrates robust performance,achieving accuracies of 94.34%,99.95%,and 87.94%on the publicly available kitsune,Bot-IoT,and UNSW-NB15 datasets,respectively.Furthermore,its ability to detect zero-day attacks is validated through evaluations on two additional benchmark datasets,TON-IoT and IoT-23,using a Deep Federated Learning(DFL)framework,underscoring the generalization and effectiveness of the model in heterogeneous and decentralized IoT environments.Experimental results demonstrate superior performance over existing methods,establishing the proposed framework as an efficient and scalable solution for IoT security.展开更多
文摘A combination of nucleos(t)ides and hepatitis B immunoglobulin(HBIg)has been found to be effective for the prevention of hepatitis B viral(HBV)reinfection after liver transplantation(LT),but its administration is costly,and not always available.We report the case of a male,33-year-old cirrhotic patient who has tested positive for serum HBsAg,and HBeAg,with 9.04×107 copies/mL of HBV DNA.He suffered from acute liver failure and was near death before undergoing emergency LT.No HBIg was available at the time,so only lamivudine was used.He routinely received immunosuppression medication.Serum HBV DNA and HBsAg still showed positive post-LT,and the graft re-infected.Hepatitis B flared three months later.Adefovir dipivoxil was added to the treatment,but in the 24th mo of treatment,the patient developed lamivudine resistance and a worsening of the hepatitis occurred shortly thereafter.The treatment combination was then changed to a double dosage of entecavir and the disease was gradually resolved.After 60-mo of post-LT nucleos(t)ide analogue therapy,anti-HBs seroconverted,and the antiviral was stopped.By the end of a 12-mo follow-up,the patient had achieved sustained recovery.In conclusion,the case seems to point to evidence that more potent and less resistant analogues like entecavir might fully replace HBIg as an HBV prophylaxis and treatment regimen.
文摘In this paper,we used higher order Haar wavelet method(HOHWM),introduced by Majak et al.[1],for approximate solution of second order integro-diferential equations(IDEs)of second-kind.It is improvement of long-established Haar wavelet collocation method(HWCM)which has been much popular among researchers and has many applications in literature.Present study aims to improve the numerical results of second order IDEs from first order rate of convergence in case of HWCM to the second and fourth order rate of convergence using HOHWM,depending on parameterλfor values 1 and 2,respectively.Several problems available in the literature of both,Volterra and Fredholm type of IDEs,are tested and compared with HWCM to illustrate the performance of our proposed method.
基金supported through theOngoing Research Funding Program(ORF-2025-498),King Saud University,Riyadh,Saudi Arabia.
文摘Android smartphones have become an integral part of our daily lives,becoming targets for ransomware attacks.Such attacks encrypt user information and ask for payment to recover it.Conventional detection mechanisms,such as signature-based and heuristic techniques,often fail to detect new and polymorphic ransomware samples.To address this challenge,we employed various ensemble classifiers,such as Random Forest,Gradient Boosting,Bagging,and AutoML models.We aimed to showcase how AutoML can automate processes such as model selection,feature engineering,and hyperparameter optimization,to minimize manual effort while ensuring or enhancing performance compared to traditional approaches.We used this framework to test it with a publicly available dataset from the Kaggle repository,which contains features for Android ransomware network traffic.The dataset comprises 392,024 flow records,divided into eleven groups.There are ten classes for various ransomware types,including SVpeng,PornDroid,Koler,WannaLocker,and Lockerpin.There is also a class for regular traffic.We applied a three-step procedure to select themost relevant features:filter,wrapper,and embeddedmethods.The Bagging classifier was highly accurate,correctly getting 99.84%of the time.The FLAML AutoML framework was evenmore accurate,correctly getting 99.85%of the time.This is indicative of howwellAutoML performs in improving things with minimal human assistance.Our findings indicate that AutoML is an efficient,scalable,and flexible method to discover Android ransomware,and it will facilitate the development of next-generation intrusion detection systems.
文摘The rapid growth of IoT networks necessitates efficient Intrusion Detection Systems(IDS)capable of addressing dynamic security threats under constrained resource environments.This paper proposes a hybrid IDS for IoT networks,integrating Support Vector Machine(SVM)and Genetic Algorithm(GA)for feature selection and parameter optimization.The GA reduces the feature set from 41 to 7,achieving a 30%reduction in overhead while maintaining an attack detection rate of 98.79%.Evaluated on the NSL-KDD dataset,the system demonstrates an accuracy of 97.36%,a recall of 98.42%,and an F1-score of 96.67%,with a low false positive rate of 1.5%.Additionally,it effectively detects critical User-to-Root(U2R)attacks at a rate of 96.2%and Remote-to-Local(R2L)attacks at 95.8%.Performance tests validate the system’s scalability for networks with up to 2000 nodes,with detection latencies of 120 ms at 65%CPU utilization in small-scale deployments and 250 ms at 85%CPU utilization in large-scale scenarios.Parameter sensitivity analysis enhances model robustness,while false positive examination aids in reducing administrative overhead for practical deployment.This IDS offers an effective,scalable,and resource-efficient solution for real-world IoT system security,outperforming traditional approaches.
基金supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2025R97)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘The exponential growth of the Internet of Things(IoT)has introduced significant security challenges,with zero-day attacks emerging as one of the most critical and challenging threats.Traditional Machine Learning(ML)and Deep Learning(DL)techniques have demonstrated promising early detection capabilities.However,their effectiveness is limited when handling the vast volumes of IoT-generated data due to scalability constraints,high computational costs,and the costly time-intensive process of data labeling.To address these challenges,this study proposes a Federated Learning(FL)framework that leverages collaborative and hybrid supervised learning to enhance cyber threat detection in IoT networks.By employing Deep Neural Networks(DNNs)and decentralized model training,the approach reduces computational complexity while improving detection accuracy.The proposed model demonstrates robust performance,achieving accuracies of 94.34%,99.95%,and 87.94%on the publicly available kitsune,Bot-IoT,and UNSW-NB15 datasets,respectively.Furthermore,its ability to detect zero-day attacks is validated through evaluations on two additional benchmark datasets,TON-IoT and IoT-23,using a Deep Federated Learning(DFL)framework,underscoring the generalization and effectiveness of the model in heterogeneous and decentralized IoT environments.Experimental results demonstrate superior performance over existing methods,establishing the proposed framework as an efficient and scalable solution for IoT security.
