Hybrid Intrusion Detection System Based on Optimal Feature Selection and Evolutionary Algorithm for Wired Networks
Journal Title: International Journal of Innovations in Science and Technology - Year 2025, Vol 7, Issue 2
Abstract
The field of cybersecurity encounters ongoing difficulties in identifying and preventing attacks in networks, and the pervasive threat of cyberattacks demands continual advancements in intrusion detection systems (IDS) to safeguard network integrity. Traditional intrusion detection systems face the challenge of class imbalance. Addressing the formidable challenges posed by class imbalance and high-dimensional data, this research proposes a novel hybrid IDS approach. Leveraging (ACO), the algorithm navigates complex datasets to identify salient features, effectively mitigating the complexities associated with high-dimensional data. Subsequently, a Weighted Stacking Classifier amalgamates the strengths of Random Forest, AdaBoost, and Gradient Boosting classifiers, fortifying the system’s ability to handle class imbalance robustly. By strategically enhancing the importance of base classifiers with favorabletraining outcomes and diminishing the influence of those yielding inferior results, the hybrid IDS endeavorsto optimize classification efficacy. The experimentation, conducted exclusively on the dataset named NSL-KDD,demonstrates the efficacy of the proposed model, yielding remarkable results. With a 90.13% Accuracy, 88.87% precision, 91.23% Recall, and 87.33% F1-score, the hybrid IDS exhibits superior performance in detecting malicious activity. The findings underscore the viability of the proposed hybrid IDS as a potent tool in the ongoing battle against cyber threats, positioning it for real-world deployment across diverse networks.
Authors and Affiliations
Husnain Babar, Muhammad Imran, Anees Tariq
Keywords
Related Articles
- EP ID EP769781
- DOI -
- Views 7
- Downloads 0
LULC-NEAT: Land Use Land Cover Classification Using NeuroEvolution of Augmenting Topologies
Introduction/Importance of Study: NEAT's potency in optimizing neural networks for accurate LULC classification, aimed at better environmental stewardship, is shown. Novelty statement: LULC-NEAT introduces NeuroEvoluti...
Exploring Character-Based Stylometry Features Using Machine Learning for Intrinsic Plagiarism Detection in Urdu
Plagiarism detection in natural language processing (NLP) plays a crucial role in maintaining textual integrity across various domains, particularly for low-resource languages like Urdu. This study addresses the emergi...
Hybrid Intrusion Detection System Based on Optimal Feature Selection and Evolutionary Algorithm for Wired Networks
The field of cybersecurity encounters ongoing difficulties in identifying and preventing attacks in networks, and the pervasive threat of cyberattacks demands continual advancements in intrusion detection sys...
Crypto Currency Compensation Model to Detect Optimal Channel of Internet of Things Through Blockchain
The ever-growing number of belongings of internet (IoT) devices in civilization creates a reliable, accessible, and safe infrastructure for processing the calculated data. One-point failures result from the prevalent I...
A Review on Cloud Computing Threats, Securityand Possible Solutions
loud computing is increasingly popular, with major companies like Microsoft, Google, and Amazon creating expansive cloud environments to support vast user bases. Despite its benefits, security remains a significant con...