Detecting Unauthorized Network Intrusion based on Network Traffic using Behavior Analysis Techniques

Nowadays, network intrusion detection is an essential problem because cyber-attacks are increasing in both the number and extent of the danger. Network intrusion techniques often use various methods to bypass the oversight of anomaly detection and surveillance systems. This paper proposes to use behavior analysis techniques, machine learning, and deep learning algorithms for the task of detecting network intrusions. The practical and scientific significance of our paper includes two issues: (1) Regarding the process of selecting and extracting features: instead of using typical abnormal behaviors of attacks, this study will use statistical behaviors that are easy to calculate and extract while still ensuring the effectiveness of the method; (2) Regarding the detection process, this study proposes to use the Random Forest (RF) classification algorithm, the Multilayer Perceptron (MLP) and the Convolutional Neural Network (CNN) deep learning model. The experimental results in Section IV have proven that our proposal in this paper is completely correct and reasonable. Based on the results shown in Section IV, this study has provided network surveillance systems with a number of abnormal behaviors as the basis for detecting network intrusions.

@article{Lam2021,
  title     = {Detecting Unauthorized Network Intrusion based on Network Traffic using Behavior Analysis Techniques},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {12},
  number    = {4},
  year      = {2021},
  publisher = {The Science and Information Organization},
  author    = {Nguyen Tung Lam},
  doi       = {10.14569/IJACSA.2021.0120407},
  url       = {https://doi.org/10.14569/IJACSA.2021.0120407}
}