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The article developed a method for automated penetration testing using deep machine learning technology. The main purpose of the development is to improve the security of computer systems. To achieve this goal, the analysis of existing penetration testing methods was carried out and their main disadvantages were identified. They are mainly related to the subjectivity of assessments in the case of manual testing. In cases of automated testing, most authors confirm the fact that there is no unified effective solution for the procedures used. This contradiction is resolved using intelligent methods of analysis. It is proposed that the developed method be based on deep reinforcement learning technology. To achieve the main goal, a study was carried out of the Shadov system's ability to collect factual data for designing attack trees, as well as the Mulval platform for generating attack trees. A method for forming a matrix of cyber intrusions using the Mulval tool has been developed. The Deep Q - Lerning Network method has been improved for analyzing the cyber intrusion matrix and finding the optimal attack trajectory. In the study, according to the deep reinforcement learning method, the reward scores assigned to each node, according to the CVSS rating, were used. This made it possible to shrink the attack trees and identify an attack with a greater likelihood of occurring. A comparative study of the automated penetration testing method was carried out. The practical possibility of using the developed method to improve the security of a computer system has been revealed.

The functions of long noncoding (lnc) RNA in the response of maize to infection by rice black streaked dwarf virus (RBSDV) is poorly understood, and therefore, a comprehensive disease‐responding lncRNA profile in maize infected with RBSDV was investigated. A total of 38,511 mRNAs and 2717 lncRNAs were identified by performing transcriptomic analysis of maize leaves infected with the virus, including 558 and 8276 differentially expressed lncRNAs (DElncRs) and mRNAs (DEmRs). Functional enrichment analysis of DEmRs showed that multiple immune‐related pathways were significantly enriched (p < 0.05), such as "plant hormone signal transduction", "phenylpropanoid biosynthesis", and "plant–pathogen interaction". There were 248 DElncR‐DEmR pairs, including 169 DElncRs and 236 target DEmRs generated by combining location information and differentially expressed transcripts in this work. In addition, we found that the promoter of lncRNA MSTRG.17772.1 that responds to RBSDV contains abscisic acid (ABA)‐ and methyl jasmonate (MeJA)‐responsive regions, and these two hormones can increase the level of MSTRG.17772.1 and corresponding target mRNA Zm00001d049643_T003, suggesting that ABA and MeJA may respond to RBSDV infection through MSTRG.17772.1–Zm00001d049643_T003 in maize. Our findings revealed the expression of RBSDV‐responsive lncRNAs in maize and provide novel insights into the connection between lncRNAs and ABA and MeJA in the regulation of plant disease resistance. [ABSTRACT FROM AUTHOR]