Ensuring network security with a robust intrusion detection system using ensemble-based machine learning

A machine learning and explainable artificial intelligence approach for predicting the efficacy of hematopoietic stem cell transplant in pediatric patients

Distributed denial-of-service (DDoS) attacks pose a significant threat to computer networks and systems by disrupting services through the saturation of targeted systems with traffic from multiple sources. Real-time detection of these attacks has become a critical cybersecurity task. However, current DDoS attack detection methods suffer from high false positive rates and limited ability to capture the complex patterns of attack traffic. This research proposes an enhanced approach for detecting DDoS attacks using a hybrid feature selection technique in combination with an ensemble-based classifiers. The ensemble-based approach aggregates many decision trees to increase classification accuracy and reduce overfitting and model robustness. The feature selection technique uses correlation analysis, mutual information, and principal component analysis to identify the most useful characteristics for attack detection. The ensemble-based Random Forest classifier from the various ensemble-based approaches with the specified relevant features produces the best detection rates. Many datasets related to identifying DDoS attacks are used to evaluate the proposed model, and experimental findings demonstrate that it surpasses existing techniques in terms of accuracy, recall, precision, f1-score, and false positive rate, with other evaluation metrics. The proposed approach achieves almost 100 % accuracy, 100 % true positive rate, and 0 % error rate making it a promising solution for DDoS attack detection. 

Enhancing DDoS attack detection with hybrid feature selection and ensemble-based classifier: A promising solution for robust cybersecurity

Feature fusion improves performance and interpretability of machine learning models in identifying soil pollution of potentially contaminated sites.

One of the major threats to computer networks and systems is distributed denial-of-service (DDoS) attacks. These attacks include saturating the targeted system with a large volume of traffic coming from several sources, which causes a service interruption. Detecting these attacks in real-time has become a critical task in cybersecurity. The existing method of DDoS attack detection suffers from the problem of high false positive rates. Additionally, the classifiers used in the existing methods may not be able to capture the complex patterns of the DDoS attack traffic, leading to low accuracy. In this research, I propose an enhanced approach for detecting DDoS attacks using an ensemble-based random forest classifier with a novel feature selection technique. The ability of the ensemble-based Random Forest Classifier to aggregate many decision trees to increase classification accuracy makes it a better option for DDoS attack detection than a single machine learning-based classifier. By lowering the variance and bias of the classifier, ensemble-based approaches are known to reduce overfitting and increase the robustness of the model. To choose the most useful characteristics for DDoS attack detection, the feature selection strategy uses a novel combination of correlation analysis, mutual information, and principal component analysis techniques. A part of the CIC-DDoS2019 dataset is used for the evaluation of the proposed method and to compare it to other modern approaches. The experimental results reveal that when integrated with additional evaluation metrics, the proposed approach outperforms existing techniques in various aspects, including accuracy, recall, precision, F1-score, false positive rate, and more. The proposed approach obtained almost 100% accuracy, 0% false positive rate, and 100% true positive rate. 

Enhanced Ensemble-Based Distributed Denial-of-Service (DDoS) Attack Detection with Novel Feature Selection: A Robust Cybersecurity Approach

Feature selection using Decomposed Mutual Information Maximization

Internet-wide traffic redirection attacks have been reported for long, and are mainly caused by Border Gateway Protocol route hijacking. Such attacks can be quite harmful, impairing access to popular Internet sites for long periods. This work addresses the use of machine learning techniques (both unsupervised and supervised) leveraging from a distributed monitoring infrastructure of probes that measure the round trip time to Internet sites under surveillance. The detection process is separated into two stages: per-probe classification and a combination of individual probe decisions. Our results show that the best strategy is to classify using an unsupervised technique based on Tukey's method and to combine using Hidden Markov Models, due to its performance and adaptability to different attack types. 

https://onlinelibrary.wiley.com/doi/pdfdirect/10.1049/ntw2.12085

Detection of Internet‐wide traffic redirection attacks using machine learning techniques

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Feature selection using Decomposed Mutual Information Maximization

Detection of Internet‐wide traffic redirection attacks using machine learning techniques