Template-Type: ReDIF-Article 1.0
Author-Name:Husnain Babar, Muhammad Imran,Anees Tariq
Author-Email:anees.tariq@szabist-isb.edu.pk
Author-Workplace-Name:Department of Robotics and Artificial Intelligence, SZABISTUniversity,Islamabad, Pakistan
Title:Hybrid Intrusion Detection System Based on Optimal Feature Selection and Evolutionary Algorithm for Wired Networks
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 importanceof  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.
Keywords:Intrusion Detection System; Ant Colony Optimization; Feature Selection; High-Dimensional Data; Weighted Stacking Classifier
Journal:International Journal of Innovations in Science and Technology
Pages:916-925
Volume:7
Issue:2
Year:2025
Month:May
File-URL:https://journal.50sea.com/index.php/IJIST/article/view/1374/1906
File-Format: Application/pdf
File-URL:https://journal.50sea.com/index.php/IJIST/article/view/1374
File-Format: text/html
Handle: RePEc:abq:IJIST:v:7:y:2025:i:2:p:916-925