Advanced Persistent Threats(APTs)represent one of the most complex and dangerous categories of cyber-attacks characterised by their stealthy behaviour,long-term persistence,and ability to bypass traditional detection ...Advanced Persistent Threats(APTs)represent one of the most complex and dangerous categories of cyber-attacks characterised by their stealthy behaviour,long-term persistence,and ability to bypass traditional detection systems.The complexity of real-world network data poses significant challenges in detection.Machine learning models have shown promise in detecting APTs;however,their performance often suffers when trained on large datasets with redundant or irrelevant features.This study presents a novel,hybrid feature selection method designed to improve APT detection by reducing dimensionality while preserving the informative characteristics of the data.It combines Mutual Information(MI),Symmetric Uncertainty(SU)and Minimum Redundancy Maximum Relevance(mRMR)to enhance feature selection.MI and SU assess feature relevance,while mRMR maximises relevance and minimises redundancy,ensuring that the most impactful features are prioritised.This method addresses redundancy among selected features,improving the overall efficiency and effectiveness of the detection model.Experiments on a real-world APT datasets were conducted to evaluate the proposed method.Multiple classifiers including,Random Forest,Support Vector Machine(SVM),Gradient Boosting,and Neural Networks were used to assess classification performance.The results demonstrate that the proposed feature selection method significantly enhances detection accuracy compared to baseline models trained on the full feature set.The Random Forest algorithm achieved the highest performance,with near-perfect accuracy,precision,recall,and F1 scores(99.97%).The proposed adaptive thresholding algorithm within the selection method allows each classifier to benefit from a reduced and optimised feature space,resulting in improved training and predictive performance.This research offers a scalable and classifier-agnostic solution for dimensionality reduction in cybersecurity applications.展开更多
This research aims to develop a model to enhance lymphatic diseases diagnosis by the use of random forest ensemble machine-learning method trained with a simple sampling scheme. This study has been carried out in two ...This research aims to develop a model to enhance lymphatic diseases diagnosis by the use of random forest ensemble machine-learning method trained with a simple sampling scheme. This study has been carried out in two major phases: feature selection and classification. In the first stage, a number of discriminative features out of 18 were selected using PSO and several feature selection techniques to reduce the features dimension. In the second stage, we applied the random forest ensemble classification scheme to diagnose lymphatic diseases. While making experiments with the selected features, we used original and resampled distributions of the dataset to train random forest classifier. Experimental results demonstrate that the proposed method achieves a remark-able improvement in classification accuracy rate.展开更多
基金funded by Universiti Teknologi Malaysia under the UTM RA ICONIC Grant(Q.J130000.4351.09G61).
文摘Advanced Persistent Threats(APTs)represent one of the most complex and dangerous categories of cyber-attacks characterised by their stealthy behaviour,long-term persistence,and ability to bypass traditional detection systems.The complexity of real-world network data poses significant challenges in detection.Machine learning models have shown promise in detecting APTs;however,their performance often suffers when trained on large datasets with redundant or irrelevant features.This study presents a novel,hybrid feature selection method designed to improve APT detection by reducing dimensionality while preserving the informative characteristics of the data.It combines Mutual Information(MI),Symmetric Uncertainty(SU)and Minimum Redundancy Maximum Relevance(mRMR)to enhance feature selection.MI and SU assess feature relevance,while mRMR maximises relevance and minimises redundancy,ensuring that the most impactful features are prioritised.This method addresses redundancy among selected features,improving the overall efficiency and effectiveness of the detection model.Experiments on a real-world APT datasets were conducted to evaluate the proposed method.Multiple classifiers including,Random Forest,Support Vector Machine(SVM),Gradient Boosting,and Neural Networks were used to assess classification performance.The results demonstrate that the proposed feature selection method significantly enhances detection accuracy compared to baseline models trained on the full feature set.The Random Forest algorithm achieved the highest performance,with near-perfect accuracy,precision,recall,and F1 scores(99.97%).The proposed adaptive thresholding algorithm within the selection method allows each classifier to benefit from a reduced and optimised feature space,resulting in improved training and predictive performance.This research offers a scalable and classifier-agnostic solution for dimensionality reduction in cybersecurity applications.
文摘This research aims to develop a model to enhance lymphatic diseases diagnosis by the use of random forest ensemble machine-learning method trained with a simple sampling scheme. This study has been carried out in two major phases: feature selection and classification. In the first stage, a number of discriminative features out of 18 were selected using PSO and several feature selection techniques to reduce the features dimension. In the second stage, we applied the random forest ensemble classification scheme to diagnose lymphatic diseases. While making experiments with the selected features, we used original and resampled distributions of the dataset to train random forest classifier. Experimental results demonstrate that the proposed method achieves a remark-able improvement in classification accuracy rate.
