Online user security is strongly reliant on a number of factors. One of the most important steps in enhancing data and communication security on well-known websites and a variety of internet services is adhering to security standards and using reliable and cutting-edge technology. These standards and technologies were developed and tested recently. Our investigation at the CERTFA Lab on 50 well-known websites shows that their security is comparable to that of the rest of the world, and very few websites are completely utilizing new security standards and technology. In fact, Russia has strong and comprehensive cyber capabilities. Websites run by the Russian government are subject to unprecedented cyberattacks, and technical measures have been taken to block international online traffic. Although domestic websites have operated successfully within the nation, some security measures need to be taken more seriously. According to our investigation, 47 of the websites we looked into have been configured with CSP2, with Yandex.ru, Ozon.ru, and wileberries.ru's implementations having more specifics and being more secure than those of the other websites, which used the upgrade-insecure-requests option as the default setting for CSP. Additionally, the results of the analysis of contemporary standards used in this study (DNSSEC, CAA, DMARC, SPF, and Expect-CT), which are mandated for the majority of Internet businesses, show that well-known Russian websites have correctly implemented these standard configurations.

Ontologies are regarded as the solution to data heterogeneity on the Semantic Web (SW), but they also suffer from the heterogeneity problem, which leads to the ambiguity of data information. Ontology Meta-Matching technique (OMM) is able to solve the ontology heterogeneity problem through aggregating various similarity measures to find the heterogeneous entities. Inspired by the success of Reinforcement Learning (RL) in solving complex optimization problems, this work proposes a RL-based OMM technique to address the ontology heterogeneity problem. First, we propose a novel RL-based OMM framework, and then, a neural network that is called evaluated network is proposed to replace the Q table when we choose the next action of the agent, which is able to reduce memory consumption and computing time. After that, to better guide the training of neural network and improve the accuracy of RL agent, we establish a memory bank to mine depth information during the evaluated network's training procedure, and we use another neural network that is called target network to save the historical parameters. The experiment uses the famous benchmark in ontology matching domain to test our approach's performance, and the comparisons among Deep Reinforcement Learning(DRL), RL and state-of-the-art ontology matching systems show that our approach is able to effectively determine high-quality alignments.

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