Malware Detection and Analysis based on AI Algorithm

In the context of the rapid development of computer technology, malware has gradually attracted widespread attention and been widely used. The research focus of this article is to explore how to use artificial intelligence algorithms to analyze data acquisition in machine learning and complete corresponding simulation tasks. First, this article introduces several mainstream methods and models that are commonly used at present, and discusses their respective advantages and disadvantages; then, this article combines specific cases to further summarize the existing machine learning methods and processes, and proposes ideas for improvement; finally, this article obtains recognition rules by comparing different feature thresholds, and calculates the recognition results based on the obtained parameters to draw conclusions. The test results show that the classification accuracy of the malware detection model is as low as 73% and as high as 94.3%; the detection accuracy is as low as 83.37% and as high as 94.6%; the recall rate is as low as 74.86% and as high as 89.47%. These results illustrate that the performance of the optimized malware detection model can meet user needs and the effect is obvious.