Malicious Traffic Detection Algorithm for the Internet of Things Based on Temporal Spatial Feature Fusion

With the rapid development of the Internet of Things, the security issues of its network environment have gradually attracted attention. To enable faster and more accurate identification and detection of malicious traffic attacks in the Internet of Things, an optimized malicious traffic detection algorithm based on fusion of temporal and spatial features is proposed. This method improves the feature extraction performance of traffic data and increases the accuracy of traffic detection. The test results showed that the comprehensive performance of the fusion algorithm was superior to the other four algorithms used for comparison. On the KDD99-CUP dataset, the F1 of the feature fusion algorithm reached 93.16%, while the F1 of algorithms 1-4 were 81.36%, 67.89%, 90.56%, and 92.24%, respectively. On the test set, 182 traffic samples were accurately identified, including 139 correctly identified malicious traffic and 43 correctly identified normal traffic, with recognition accuracy of 98.73% and 97.65%, respectively. Experimental results revealed that the use of fused feature extraction in traffic detection systems could improve detection efficiency and accuracy, providing a safer and more reliable guarantee for the interaction process of the Internet of Things network, and safeguarding the rapid development and application of the Internet of Things.

@article{Zhang2024,
  title     = {Malicious Traffic Detection Algorithm for the Internet of Things Based on Temporal Spatial Feature Fusion},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {11},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Linzhong Zhang},
  doi       = {10.14569/IJACSA.2024.0151162},
  url       = {https://doi.org/10.14569/IJACSA.2024.0151162}
}