Search

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.

Testing process for penetration into computer systems mathematical model was developed in the article. The proposed model differs from the known by computer systems specialized information platforms security testing capabilities, which made it possible to estimate the penetration test algorithm execution time falling within a given interval probability. The proposed testing process for penetration into computer systems mathematical model was further developed (modified). Modified model distinctive feature is the Erlang distribution as the main one in the state transition processes mathematical formalization. This made it possible on the one hand to unify the mathematical model and present the testing process at a higher level of the testing hierarchy, on the other hand to simplify it 1.7 times. A security testing mathematical model was developed in order to estimate the simulation results accuracy, based on the known GERT-networks simplification and modification approach. Testing algorithms execution time value mathematical expectation values are obtained and estimated. Comparative modeling results investigations have shown the study values comparability for all three approaches of security testing process mathematical formalization. This confirmed the hypothesis that it is advisable to use a unified mathematical formalization approach, which was implemented in a penetration testing process modified mathematical model.

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]

Nonspecific lipidtransfer proteins (nsLTPs), which are small, cysteine‐rich proteins, belong to the pathogenesis‐related protein family, and several of them act as positive regulators during plant disease resistance. However, the underlying molecular mechanisms of these proteins in plant immune responses are unclear. In this study, a typical nsLTP gene, StLTP10, was identified and functionally analysed in potato. StLTP10 expression was significantly induced by Phytophthora infestans, which causes late blight in potato, and defence‐related phytohormones, including abscisic acid (ABA), salicylic acid, and jasmonic acid. Characterization of StLTP10‐overexpressing and knockdown lines indicated that StLTP10 positively regulates plant resistance to P. infestans. This resistance was coupled with enhanced expression of reactive oxygen species scavenging‐ and defence‐related genes. Furthermore, we identified that StLTP10 physically interacts with ABA receptor PYL4 and affects its subcellular localization. These two proteins work together to regulate stomatal closure during pathogen infection. Interestingly, we also found that wound‐induced protein kinase interacts with StLTP10 and positively regulates its protein abundance. Taken together, our results provide insight into the role of StLTP10 in resistance to P. infestans and suggest candidates to enhance broad‐spectrum resistance to pathogens in potato. [ABSTRACT FROM AUTHOR]