Currently, alongside the rapid development of information and communication technologies, not only the number of malware programs but also their functionality is increasing. This necessitates the use of intelligent approaches to detecting unknown or emerging malware. This article proposes a model of an artificial immune system based on the principles of the biological immune system. The proposed model emphasizes the need to utilize mechanisms such as the formation of immune detectors, their training and selection, cloning and mutation, and the formation of immunological memory. The Learning Vector Quantization neural network algorithm is proposed for creating detectors.
| Mualliflar | Umarov, Shukhrat, Umarova, Munojatxon, Valiyev, Abdulaziz |
|---|---|
| Jurnal | Al-Farg'oniy avlodlari |
| Nashr sanasi | 2026-03-18 |
| Son | 1 |
| Betlar | 241-246 |
| Til | Rus |
artificial immune system, computer viruses, malware, information security, neural networks, Learning Vector Quantization, immune detectors, anomaly detection, cybersecurity, immunological memory., искусственная иммунная система, компьютерные вирусы, вредоносные программы, информационная безопасность, нейронные сети, Learning Vector Quantization, иммунные детекторы, обнаружение аномалий, кибербезопасность, иммунологическая память., sun’iy immun tizimi, kompyuter viruslari, zararli dasturlar, axborot xavfsizligi, neyron tarmoqlar, Learning Vector Quantization, immun detektorlari, anomaliyalarni aniqlash, kiberxavfsizlik, immun xotira
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