A Comprehensive Analysis of Network Security Attack Classification using Machine Learning Algorithms

As internet usage and connected devices continue to proliferate, the concern for network security among individuals, businesses, and governments has intensified. Cybercriminals exploit these opportunities through various attacks, including phishing emails, malware, and DDoS attacks, leading to disruptions, data exposure, and financial losses. In response, this study investigates the effectiveness of machine learning algorithms for enhancing intrusion detection systems in network security. Our findings reveal that Random Forest demonstrates superior performance, achieving 90% accuracy and balanced precision-recall scores. KNN exhibits robust predictive capabilities, while Logistic Regression delivers commendable accuracy, precision, and recall. However, Naive Bayes exhibits slightly lower performance compared to other algorithms. The study underscores the significance of leveraging advanced machine learning techniques for accurate intrusion detection, with Random Forest emerging as a promising choice. Future research directions include refining models and exploring novel approaches to further enhance network security.

@article{Alqahtani2024,
  title     = {A Comprehensive Analysis of Network Security Attack Classification using Machine Learning Algorithms},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {4},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Abdulaziz Saeed Alqahtani and Osamah A. Altammami and Mohd Anul Haq},
  doi       = {10.14569/IJACSA.2024.01504127},
  url       = {https://doi.org/10.14569/IJACSA.2024.01504127}
}