Application of network security penetration technology in power internet of things security vulnerability detection

The emergence of network penetration attacks makes network security problems more and more prominent. To better detect network penetration attacks, this article first proposes an attack detection method based on ant colony classification rules mining algorithm based on swarm intelligence theory. Classification rules perform pattern matching to detect attack behavior. Second, the quantitative problem in the evaluation mechanism of power internet security vulnerability is analyzed. The addition and deletion of power internet of things (IoT) security assessment elements introduces a probabilistic computing model, improves the vulnerability assessment mechanism of power object networks, and solves the problem of power object network security and quantitative computing. Finally, after performing a series of preprocessing on the dataset, using the above method to find the classification rule or classification center, respectively, perform pattern matching or similarity calculation, and finally obtain the attack detection result. A series of evaluation functions are established in the experiment, and the experimental results are compared with other related algorithms. The results show that the method can effectively detect network penetration attacks and IoT security vulnerabilities, and the effectiveness of attack detection methods is greatly improved.