This study introduces an innovative hybrid approach that integrates deep learning with blockchain technology to improve cybersecurity,focusing on network intrusion detection systems(NIDS).The main goal is to overcome ...This study introduces an innovative hybrid approach that integrates deep learning with blockchain technology to improve cybersecurity,focusing on network intrusion detection systems(NIDS).The main goal is to overcome the shortcomings of conventional intrusion detection techniques by developing amore flexible and robust security architecture.We use seven unique machine learning models to improve detection skills,emphasizing data quality,traceability,and transparency,facilitated by a blockchain layer that safeguards against datamodification and ensures auditability.Our technique employs the Synthetic Minority Oversampling Technique(SMOTE)to equilibrate the dataset,therefore mitigating prevalent class imbalance difficulties in intrusion detection.The model selection procedure determined that Random Forest was the most successful model,with a notable detection accuracy of 97%.This substantially surpasses conventional methods and enhances the system’s capacity to identify both established and novel threats with exceptional accuracy.To optimize feature selection and maximize performance,we use Extreme Gradient Boosting(XGBoost),which improves the significance of chosen features while reducing the danger of overfitting.Our study indicates that the integrated use of machine learning for pattern identification,multi-factor authentication(MFA)for access security,and blockchain for data validation constitutes a thorough and sustainable cybersecurity solution.This architecture not only increases security but also lowers the need for regular human monitoring,significantly cutting energy consumption connected with cybersecurity infrastructure.The research finds that this integrated strategy provides a realistic road for increasing network security,addressing real-world cyber threats,and promoting eco-friendly practices in IT security.展开更多
The cohort intelligence （CI） method has recently evolved as an optimization method based on artificial intelligence. We use the CI method for the first time to optimize the parameters of the fractional proportional-...The cohort intelligence （CI） method has recently evolved as an optimization method based on artificial intelligence. We use the CI method for the first time to optimize the parameters of the fractional proportional- integral-derivative （PID） controller. The performance of the CI method in designing the fractional PID controller was validated and compared with those of some other popular algorithms such as particle swarm optimization, the genetic algorithm, and the improved electromagnetic algorithm. The CI method yielded improved solutions in terms of the cost function, computing time, and function evaluations in comparison with the other three algorithms. In addition, the standard deviations of the CI method demonstrated the robustness of the proposed algorithm in solving control problems.展开更多
文摘This study introduces an innovative hybrid approach that integrates deep learning with blockchain technology to improve cybersecurity,focusing on network intrusion detection systems(NIDS).The main goal is to overcome the shortcomings of conventional intrusion detection techniques by developing amore flexible and robust security architecture.We use seven unique machine learning models to improve detection skills,emphasizing data quality,traceability,and transparency,facilitated by a blockchain layer that safeguards against datamodification and ensures auditability.Our technique employs the Synthetic Minority Oversampling Technique(SMOTE)to equilibrate the dataset,therefore mitigating prevalent class imbalance difficulties in intrusion detection.The model selection procedure determined that Random Forest was the most successful model,with a notable detection accuracy of 97%.This substantially surpasses conventional methods and enhances the system’s capacity to identify both established and novel threats with exceptional accuracy.To optimize feature selection and maximize performance,we use Extreme Gradient Boosting(XGBoost),which improves the significance of chosen features while reducing the danger of overfitting.Our study indicates that the integrated use of machine learning for pattern identification,multi-factor authentication(MFA)for access security,and blockchain for data validation constitutes a thorough and sustainable cybersecurity solution.This architecture not only increases security but also lowers the need for regular human monitoring,significantly cutting energy consumption connected with cybersecurity infrastructure.The research finds that this integrated strategy provides a realistic road for increasing network security,addressing real-world cyber threats,and promoting eco-friendly practices in IT security.
文摘The cohort intelligence （CI） method has recently evolved as an optimization method based on artificial intelligence. We use the CI method for the first time to optimize the parameters of the fractional proportional- integral-derivative （PID） controller. The performance of the CI method in designing the fractional PID controller was validated and compared with those of some other popular algorithms such as particle swarm optimization, the genetic algorithm, and the improved electromagnetic algorithm. The CI method yielded improved solutions in terms of the cost function, computing time, and function evaluations in comparison with the other three algorithms. In addition, the standard deviations of the CI method demonstrated the robustness of the proposed algorithm in solving control problems.
