Application of Ant Colony Optimization Improved Clustering Algorithm in Malicious Software Identification

Due to the increasing threat of malware to computer systems and networks, traditional malware detection and recognition technologies face difficulties and limitations. Therefore, exploring new methods to improve the accuracy and efficiency of malware identification has become an urgent need. This study introduces ant colony algorithm to optimize traditional clustering algorithms and algorithm parameters. The experimental results showed that the improvement rates of the improved algorithm in accuracy, echo value, and false alarm rate were 0.253, 0.115, and 0.056, respectively. The accuracy on the training and validation sets continued to increase and the loss curve continued to decrease. In addition, the improved algorithm had stronger modeling ability for data feature relationships and temporal information. This is of great help in improving the recognition ability of virus and worm software. The improved algorithm had a lower occupancy rate of computing resources compared to other algorithms, but it could also effectively monitor device operation. Compared with traditional methods, this method can more accurately identify malicious software and effectively identify malicious software samples from large-scale datasets. This is of great significance for protecting computer systems and network security.

@article{Qian2024,
  title     = {Application of Ant Colony Optimization Improved Clustering Algorithm in Malicious Software Identification},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {1},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Yong Qian},
  doi       = {10.14569/IJACSA.2024.01501102},
  url       = {https://doi.org/10.14569/IJACSA.2024.01501102}
}