Sep 2026· 2026 9th International Conference on Computer Network, Electronic and Automation (ICCNEA)· pp. 1-7· 0 citations· 11 references
Abstract
Network intrusion detection systems (IDS) continue to struggle with redundant records, imbalanced attack classes, and limited model transparency. This paper presents a robust ensemble-learning framework for intrusion detection built on the CICIDS2017 dataset, and it incorporates removal of duplicate flows, handling of missing values, and filtering of extremely rare attack categories. Three classifiers Decision Tree, Random Forest, and XGBoost are evaluated on the binary detection task, after which XGBoost is extended further to a multiclass setting spanning 11 traffic categories. In the experiments, XGBoost reaches 99.89% accuracy for binary classification and 99.71% accuracy (Kappa = 0.9967) for the multiclass task. A duplicate-removal sensitivity test confirms that these results are robust, while a feature-importance analysis reveals that packet-level statistical properties are the strongest indicators of malicious traffic, producing a scalable and interpretable IDS framework overall.
The findings support the adoption of hybrid IDS architectures as a balanced and practical solution that enhances detection capability, adaptability, and reliability in evolving cyber threat landscapes.
Bang-Chen Yu· Technologique: A Global Jour...· 0 citations
Machine learning (ML) and deep learning (DL) have dominated Intrusion Detection System (IDS) research in recent years. Unfortunately, many existing studies have produced inflated results and unreliable benchmarks due to critical oversights and mistakes in the ML and DL pipeline, from data collection and labeling to fea...
Machine-learning intrusion detectors often report near-perfect performance on familiar benchmarks, yet their generalization and decision transparency remain uncertain. This study evaluates a reproducible and explainable binary network-intrusion-detection workflow using the NSL-KDD KDDTrain+_20Percent file (25,192 recor...
Dawlat Mustafa Sulaiman· Dasinya Journal for Engineer...· 0 citations
Network intrusion detection remains an important task in communication and information security, especially in scenarios where previously unseen or weakly represented attacks may not be captured by signature-based methods. This paper presents a comparative evaluation of three unsupervised anomaly detection models, name...
S. Syzranova, Matus Pleva, Maroš Harahus· International Conference Rad...· 0 citations
As developing cyber threats complexity and sophistication, traditional Network Intrusion Detection Systems (NIDS) face difficulties in detecting attacks within an acceptable time. For these challenges, this paper presents a new flow-based NIDS framework utilizing the benefits of Advanced Machine Learning methods, speci...
Omar Abdulmunem Ibrahim Aldabbagh· Al-Rafidain Engineering Jour...· 0 citations
Existing assessments of machine-learning-based intrusion detection systems (IDSs) often consider each of accuracy, reliability of probability outputs, and transparency of decisions separately. Few attempt to combine all three dimensions in a single assessment. This paper provides a reproducible IDS pipeline that satisf...
