The paper considers methods of countering speech synthesis attacks on voice biometric systems in banking. Voice biometrics security is a large-scale problem significantly raised over the past few years. Automatic speaker verification systems (ASV) are vulnerable to various types of spoofing attacks: impersonation, replay attacks, voice conversion, and speech synthesis attacks. Speech synthesis attacks are the most dangerous as the technologies of speech synthesis are developing rapidly (GAN, Unit selection, RNN, etc.). Anti-spoofing approaches can be based on searching for phase and tone frequency anomalies appearing during speech synthesis and on a preliminary knowledge of the acoustic differences of specific speech synthesizers. ASV security remains an unsolved problem, because there is no universal solution that does not depend on the speech synthesis methods used by the attacker. In this paper, we provide the analysis of existing speech synthesis technologies and the most promising attacks detection methods for banking and financial organizations. Identification features should include emotional state and cepstral characteristics of voice. It is necessary to adjust the user's voiceprint regularly. Analyzed signal should not be too smooth and containing unnatural noises or sharp interruptions changes in the signal level. Analysis of speech intelligibility and semantics are also important. Dynamic passwords database should contain words that are difficult to synthesize and pronounce. The proposed approach could be used for design and development of authentication systems for banking and financial organizations resistant to speech synthesis attacks. Keywords biometrics, automatic speaker verification, banking authentication, synthetic speech, spoofing detection Acknowledgements The paper was prepared at ITMO University within the framework of the scientific project No. 50449 "Development of cyberspace protection algorithms for solving applied problems of ensuring cybersecurity of banking organizations".
Human capital is linked to intellectual capital, so professional education is an important element in the process of training future professionals ready to develop innovations. The synergy of university education and work-based learning is necessary for the development of human capital. The creation of integrated educational projects takes into account this feature. To study the impact of these projects on human capital development, the authors conducted a four-stage research. According to the results of the study, universities want to realize their scientific potential and prepare in-demand young professionals, businesses need to hire experienced professionals focused on innovation. The needs of a young specialist are obtaining relevant knowledge and practical skills for employment. Integrated educational projects bring together numerous audiences, contribute to meeting their needs by connecting academic education, practice-oriented knowledge and practical skills through various learning formats. These projects contribute to the development of human capital, as the participants-students of these projects are more prepared for professional activities. Universities with the help of such projects develop a partner network, increase their own attractiveness and the overall level of knowledge of students. Business companies receive young professionals with practical skills and developed creative thinking.
In today's world, everything is connected via the Internet. Smart cities are one application of the Internet of Things (IoT) that is aimed at making city management more efficient and effective. However, IoT devices within a smart city may collect sensitive information. Protecting sensitive information requires maintaining privacy. Existing smart city solutions have been shown not to offer effective privacy protection. We propose a novel continuous method called Differential Privacy-Preserving Smart City (DPSmartCity). When the IoT device produces sensitive data, it applies differential privacy techniques as a privacy-preserving method that uses Laplace distributions or exponential distributions. The controller receives the perturbed data and forwards it to the SDN. SDN controllers eventually send the data to the cloud for further analysis. Accordingly, if the data is not sensitive, it is directly uploaded to the cloud. In this way, DPSmartCity provides a dynamic environment from the point of view of privacy preservation. As a result, adversaries are unable to easily compromise the privacy of the devices. The solution incurs at most 10-18% overhead on IoT devices. Our solution can therefore be used for IoT devices that are capable of handling this overhead.
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