TESTING NEURAL NETWORK MODELS FOR SOLVING THE PROBLEM OF DETECTING INFECTED PCS BASED ON DIGITAL TRACES

The development of artificial intelligence has made great progress and already today has a significant impact on a large number of industries and with the development of LLM will have an even greater impact in the future, especially on cybersecurity. AI can both help save data by early detection of cyberattacks, and harm cybersecurity by facilitating the writing of convincing phishing emails, reproducing fragments of malicious code, helping to identify weak points in the network, and finding vulnerabilities in the operating system, programs, etc. that are still unknown to software manufacturers (zero day vulnerability). Therefore, in order not to be lagging behind in this "arms race", it is necessary to already implement AI as one of the components of cyber protection in the enterprise. The relevance of the work lies in the need to find such artificial intelligence models that can already be involved in solving the problems of protecting infocommunication networks. The purpose of the article is to test neural network models of the GGUF format to assess the possibility of their application in solving the problem of detecting infected PCs based on digital traces. The paper considers