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
BACKGROUND Mucopolysaccharidosis type Ⅱ(MPS Ⅱ)is a chronic inherited disease with multiorgan involvement,a progressive course,and restricted life expectancy.AIM To evaluate the predictors of fatal outcomes in MPS Ⅱ...BACKGROUND Mucopolysaccharidosis type Ⅱ(MPS Ⅱ)is a chronic inherited disease with multiorgan involvement,a progressive course,and restricted life expectancy.AIM To evaluate the predictors of fatal outcomes in MPS Ⅱ patients.METHODS In the retrospective cohort study,the clinical,laboratory data and enzyme replacement therapy(ERT)(84.2%)of about 160 patients were extracted and analyzed from the Russian MPS Ⅱ registry,with death as a primary outcome.We compared patients who died(n=20;12.5%)with severe form(n=13;68.4%)and attenuated form(n=6,31.6%)to 140 alive patients.RESULTS Fatal outcomes occurred in 5%,35%,20%,and 40%of patients before 10,10-14,15-19,and≥20 years.The most common causes of death were cardiovascular(29.4%),respiratory failure(17.6%),including pneumonia(17.6%),and their associations(17.6%)and MPS Ⅱ progression(11.8%).Acute or chronic respiratory failure was in 53%.Died patients had higher birth weight,higher age of diagnosis,and start of ERT.Hydrocephalus,hydrocephalus bypass surgery,epilepsy,difficulty swallowing,and impaired movement after 12 years of age were significantly more common in the deceased patients.Cox regression analysis has revealed the following time-dependent covariates of the lethal outcome:1^(st)-year psychomotor development delay,delayed mental and speech development,hydrocephalus,swallow disorders,impossible walking at age>12 years,respiratory disorders,tracheostomy,neuronopathic form.CONCLUSION Increased birth weight,delayed diagnosis and the start of ERT,and development of neuronopathic form with impossible walking after 12 years were the main predictors of the fatal outcome.展开更多
Stimulated Raman scattering(SRS)under a new ignition path that combines the advantages of direct-drive(DD)and indirect-drive(ID)schemes is investigated experimentally at the Shenguang-100 kJ facility.The results show ...Stimulated Raman scattering(SRS)under a new ignition path that combines the advantages of direct-drive(DD)and indirect-drive(ID)schemes is investigated experimentally at the Shenguang-100 kJ facility.The results show that collective SRS in the plasma produced by ablating a polyimide film is detected for the ID beams,but is suppressed by adding a toe before the main pulse of the ID beams.The toe also strongly influences SRS of both the ID and DD beams excited in the plasma generated in the hohlraum.When a toe is used,the SRS spectra of the DD beams show that SRS tends to be excited in lower plasma density,which will result in a lower risk of super-hot electrons.Measurements of hot electrons support this conclusion.This research will help us produce a better pulse design for this new ignition path.展开更多
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.
文摘BACKGROUND Mucopolysaccharidosis type Ⅱ(MPS Ⅱ)is a chronic inherited disease with multiorgan involvement,a progressive course,and restricted life expectancy.AIM To evaluate the predictors of fatal outcomes in MPS Ⅱ patients.METHODS In the retrospective cohort study,the clinical,laboratory data and enzyme replacement therapy(ERT)(84.2%)of about 160 patients were extracted and analyzed from the Russian MPS Ⅱ registry,with death as a primary outcome.We compared patients who died(n=20;12.5%)with severe form(n=13;68.4%)and attenuated form(n=6,31.6%)to 140 alive patients.RESULTS Fatal outcomes occurred in 5%,35%,20%,and 40%of patients before 10,10-14,15-19,and≥20 years.The most common causes of death were cardiovascular(29.4%),respiratory failure(17.6%),including pneumonia(17.6%),and their associations(17.6%)and MPS Ⅱ progression(11.8%).Acute or chronic respiratory failure was in 53%.Died patients had higher birth weight,higher age of diagnosis,and start of ERT.Hydrocephalus,hydrocephalus bypass surgery,epilepsy,difficulty swallowing,and impaired movement after 12 years of age were significantly more common in the deceased patients.Cox regression analysis has revealed the following time-dependent covariates of the lethal outcome:1^(st)-year psychomotor development delay,delayed mental and speech development,hydrocephalus,swallow disorders,impossible walking at age>12 years,respiratory disorders,tracheostomy,neuronopathic form.CONCLUSION Increased birth weight,delayed diagnosis and the start of ERT,and development of neuronopathic form with impossible walking after 12 years were the main predictors of the fatal outcome.
基金supported by the National Natural Science Foundation of China(Grant Nos.12205274,12275251,12105270,12205272,12305262,and 12035002)the National Key Laboratory of Plasma Physics(Grant No.JCKYS2024212803)+2 种基金the Fund of the National Key Laboratory of Plasma Physics(Grant No.6142A04230103)the National Key R&D Program of China(Grant No.2023YFA1608400)the National Security Academic Fund(Grant No.U2430207).
文摘Stimulated Raman scattering(SRS)under a new ignition path that combines the advantages of direct-drive(DD)and indirect-drive(ID)schemes is investigated experimentally at the Shenguang-100 kJ facility.The results show that collective SRS in the plasma produced by ablating a polyimide film is detected for the ID beams,but is suppressed by adding a toe before the main pulse of the ID beams.The toe also strongly influences SRS of both the ID and DD beams excited in the plasma generated in the hohlraum.When a toe is used,the SRS spectra of the DD beams show that SRS tends to be excited in lower plasma density,which will result in a lower risk of super-hot electrons.Measurements of hot electrons support this conclusion.This research will help us produce a better pulse design for this new ignition path.
