Green technology is viewed as a means of creating a sustainable society and a catalyst for sustainable development by the global community. It is responsible for both the potential reduction of production waste and the reduction of carbon footprint and CO2 emissions. However, alongside with the growing popularity of green technologies, there is an emerging skepticism about their contribution to solving environmental challenges. This article focuses on three areas of eco-innovation in green technology: renewable energy, hydrogen power, and decarbonization. Our main goal is to analyze the relationship between publication activity and the number of patented research results, thus shedding light on the real-world applicability of scientific outcomes. We used several bibliometric methods for analyzing global publication and patent activity, applied to the Scopus citation database and the European Patent Office's patent database. Our results show that the advancement of research in all three areas of eco-innovation does not automatically lead to the increase in the number of patents. We offer possible reasons for such dependency based on the observations of the worldwide tendencies in green innovation sphere.
In the authors' opinion, anomaly detection systems, or ADS, seem to be the most perspective direction in the subject of attack detection, because these systems can detect, among others, the unknown (zero-day) attacks. To detect anomalies, the authors propose to use machine synesthesia. In this case, machine synesthesia is understood as an interface that allows using image classification algorithms in the problem of detecting network anomalies, making it possible to use non-specialized image detection methods that have recently been widely and actively developed. The proposed approach is that the network traffic data is "projected" into the image. It can be seen from the experimental results that the proposed method for detecting anomalies shows high results in the detection of attacks. On a large sample, the value of the complex efficiency indicator reaches 97%.