On Real-Time Model Inversion Attacks Detection

The article deals with the issues of detecting adversarial attacks on machine learning models. In the most general case, adversarial attacks are special data changes at one of the stages of the machine learning pipeline, which are designed to either prevent the operation of the machine learning system, or, conversely, achieve the desired result for the attacker. In addition to the well-known poisoning and evasion attacks, there are also forms of attacks aimed at extracting sensitive information from machine learning models. These include model inversion attacks. These types of attacks pose a threat to machine learning as a service (MLaaS). Machine learning models accumulate a lot of redundant information during training, and the possibility of revealing this data when using the model can become a serious problem.

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On the Automated Text Report Generation in Open Transport Data Analysis Platform

According to UN studies, more than 60% of the population will live in cities by 2030. It is important to receive reports on changes in the transport behavior of city residents to change existing and build new transport systems. In this article, we talk about the automated report generation module for an open platform for transport data analysis. We give the classification of transport changes in the city. The work of the module is demonstrated for various types of traffic changes in the example of the analysis of changes in traffic flows in Moscow during the celebrations in early May and the closure of the Smolenskaya metro station for long-term reconstruction. Possibilities of the analysis on time intervals of various extents and lobbies are shown. Proposed ways to further automate the management of transport systems using data collected by the platform. Possible directions for further development of an open platform for transport data analysis are proposed.

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