Jul 2026· Signal Processing and Communications Applications Conference· pp. 1-4· 0 citations· 14 references
Abstract
With the expansion of the Internet of Things (IoT) and cloud computing networks, the volume and complexity of cyberattacks have increased. Traditional signature-based intrusion detection systems (IDS) are insufficient, especially in unstable datasets and complex attack vectors. In this study, a hybrid ensemble learning model combining Random Forest (RF) and Extreme Gradient Boosting (XGBoost) algorithms is proposed to improve network security. The model makes decisions by weighting the probability scores of the classifiers using a "Soft Voting" mechanism. In experiments conducted on the NSL-KDD dataset, the proposed model achieved a 99.91% accuracy rate, surpassing most current (2024-2025) studies in literature. Furthermore, feature importance analysis is performed to increase the model's transparency, and the 20 most critical features are identified. Finally, the data space is visualized in 3D using the t-SNE algorithm, and it has been observed that the attack classes are separable.
This study proposes a feature selection approach based on Ant Colony Optimization (ACO) to identify the most relevant features for anomaly-based intrusion detection system (IDS) and reduces the feature set to 10 from the original datasets while achieving 100% detection accuracy and minimal training and detection times.
H. Talabani, Zrar Khalid Abdul, Hardi Mohammed Mohammed Saleh· Cluster Computing· 0 citations
With the proliferation of internet-connected infrastructures and the complexity of cyberattacks, cybersecurity and intelligent intrusion detection systems have become more and more critical. Intrusion detection datasets, however, are now highly imbalanced, and conventional machine learning models have become biased tow...
The increasing frequency and sophistication of cyber threats have highlighted the need for effective Intrusion Detection Systems (IDS) capable of accurately identifying malicious network traffic. Traditional rule-based frameworks often face limitations in detecting previously unseen attacks and handling high-dimensiona...
Jennifer Adelia Putri, A. Taqwa, A. Handayani· bit-Tech· 0 citations
The rapid propagation of Internet of Things (IoT) devices has significantly expanded the cyber-attack surface, particularly in essential infrastructure sectors such as energy, water, and healthcare. Machine learning (ML) based intrusion detection systems (IDS) offer a promising defense, but their real-world deployment...
Nooruddine F. Assarwie, F. Alqasemi, Tasnim M. Al-Khawlani et al.· 2026 6th International Confe...· 0 citations
The findings indicate that the RF–SVM hybrid model provides an effective and scalable solution for real-time intrusion detection in modern cybersecurity environments.
Esther J., Grace Phiri, Arockia Venice J.· International Journal of Dat...· 0 citations
The majority of assaults in heterogeneous networks are detected by intrusion detection systems (IDS). Cyberattack kinds that seriously harm networks are difficult for conventional IDSs to detect. The majority of existing solutions rely on deep learning models, which have a significant computational and energy overhead...
Abhinay Kumar Reddy Seella, Rupesh Shirke, Vijay Kumar Kasuba et al.· International Conference on...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
