Modern intrusion detection systems(MIDS)face persistent challenges in coping with the rapid evolution of cyber threats,high-volume network traffic,and imbalanced datasets.Traditional models often lack the robustness a...Modern intrusion detection systems(MIDS)face persistent challenges in coping with the rapid evolution of cyber threats,high-volume network traffic,and imbalanced datasets.Traditional models often lack the robustness and explainability required to detect novel and sophisticated attacks effectively.This study introduces an advanced,explainable machine learning framework for multi-class IDS using the KDD99 and IDS datasets,which reflects real-world network behavior through a blend of normal and diverse attack classes.The methodology begins with sophisticated data preprocessing,incorporating both RobustScaler and QuantileTransformer to address outliers and skewed feature distributions,ensuring standardized and model-ready inputs.Critical dimensionality reduction is achieved via the Harris Hawks Optimization(HHO)algorithm—a nature-inspired metaheuristic modeled on hawks’hunting strategies.HHO efficiently identifies the most informative features by optimizing a fitness function based on classification performance.Following feature selection,the SMOTE is applied to the training data to resolve class imbalance by synthetically augmenting underrepresented attack types.The stacked architecture is then employed,combining the strengths of XGBoost,SVM,and RF as base learners.This layered approach improves prediction robustness and generalization by balancing bias and variance across diverse classifiers.The model was evaluated using standard classification metrics:precision,recall,F1-score,and overall accuracy.The best overall performance was recorded with an accuracy of 99.44%for UNSW-NB15,demonstrating the model’s effectiveness.After balancing,the model demonstrated a clear improvement in detecting the attacks.We tested the model on four datasets to show the effectiveness of the proposed approach and performed the ablation study to check the effect of each parameter.Also,the proposed model is computationaly efficient.To support transparency and trust in decision-making,explainable AI(XAI)techniques are incorporated that provides both global and local insight into feature contributions,and offers intuitive visualizations for individual predictions.This makes it suitable for practical deployment in cybersecurity environments that demand both precision and accountability.展开更多
Using the spray pyrolysis process,the work shows the production of pristine and 6%Ag-doped methylammonium lead iodide bromide(MAPbI2Br)film.Through X-ray diffraction analysis,it was found that Ag doping led to a signi...Using the spray pyrolysis process,the work shows the production of pristine and 6%Ag-doped methylammonium lead iodide bromide(MAPbI2Br)film.Through X-ray diffraction analysis,it was found that Ag doping led to a significant increase in grain size to 29.64 nm,alongside reductions in dislocation line density to 5.39×1014 m−2 and d-spacing to 3.18Å,while maintaining the native cubic crystal structure of MAPbI2Br.This research demonstrates a reduction in deep-level trap states with Ag doping,along with a significant narrowing of the band gap to 1.91 eV in the 6%Ag-doped MAPbI2Br.Moreover,the refractive index and extinction coefficient increased to 2.54 and 2.13,respectively.Regarding solar cell performance,all cells demonstrated encouraging outcomes;still,the 6%Ag-doped cell distinguished itself with a fill factor of 0.82,an open-circuit voltage of 1.07 V,an outstanding short-circuit current density of 11.31 mA/cm²,and an efficiency of 10.03%.These results highlight the effectiveness of Ag doping in improving perovskite solar cell technology,marking a notable progress in this field.展开更多
基金funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R104)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Modern intrusion detection systems(MIDS)face persistent challenges in coping with the rapid evolution of cyber threats,high-volume network traffic,and imbalanced datasets.Traditional models often lack the robustness and explainability required to detect novel and sophisticated attacks effectively.This study introduces an advanced,explainable machine learning framework for multi-class IDS using the KDD99 and IDS datasets,which reflects real-world network behavior through a blend of normal and diverse attack classes.The methodology begins with sophisticated data preprocessing,incorporating both RobustScaler and QuantileTransformer to address outliers and skewed feature distributions,ensuring standardized and model-ready inputs.Critical dimensionality reduction is achieved via the Harris Hawks Optimization(HHO)algorithm—a nature-inspired metaheuristic modeled on hawks’hunting strategies.HHO efficiently identifies the most informative features by optimizing a fitness function based on classification performance.Following feature selection,the SMOTE is applied to the training data to resolve class imbalance by synthetically augmenting underrepresented attack types.The stacked architecture is then employed,combining the strengths of XGBoost,SVM,and RF as base learners.This layered approach improves prediction robustness and generalization by balancing bias and variance across diverse classifiers.The model was evaluated using standard classification metrics:precision,recall,F1-score,and overall accuracy.The best overall performance was recorded with an accuracy of 99.44%for UNSW-NB15,demonstrating the model’s effectiveness.After balancing,the model demonstrated a clear improvement in detecting the attacks.We tested the model on four datasets to show the effectiveness of the proposed approach and performed the ablation study to check the effect of each parameter.Also,the proposed model is computationaly efficient.To support transparency and trust in decision-making,explainable AI(XAI)techniques are incorporated that provides both global and local insight into feature contributions,and offers intuitive visualizations for individual predictions.This makes it suitable for practical deployment in cybersecurity environments that demand both precision and accountability.
基金supported by King Saud University,Riyadh,Saudi Arabia,under project number RSP2024R397CONAHCYT for the scholarship awarded.
文摘Using the spray pyrolysis process,the work shows the production of pristine and 6%Ag-doped methylammonium lead iodide bromide(MAPbI2Br)film.Through X-ray diffraction analysis,it was found that Ag doping led to a significant increase in grain size to 29.64 nm,alongside reductions in dislocation line density to 5.39×1014 m−2 and d-spacing to 3.18Å,while maintaining the native cubic crystal structure of MAPbI2Br.This research demonstrates a reduction in deep-level trap states with Ag doping,along with a significant narrowing of the band gap to 1.91 eV in the 6%Ag-doped MAPbI2Br.Moreover,the refractive index and extinction coefficient increased to 2.54 and 2.13,respectively.Regarding solar cell performance,all cells demonstrated encouraging outcomes;still,the 6%Ag-doped cell distinguished itself with a fill factor of 0.82,an open-circuit voltage of 1.07 V,an outstanding short-circuit current density of 11.31 mA/cm²,and an efficiency of 10.03%.These results highlight the effectiveness of Ag doping in improving perovskite solar cell technology,marking a notable progress in this field.
