Theoretical and Applied Cybersecurity (TACS)

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https://ela.kpi.ua/handle/123456789/76944

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Перегляд Theoretical and Applied Cybersecurity (TACS) за Автор "Venherskyi, Petro"

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    Comparative Analysis of the Time Stability of CNN, LSTM and DistilBERT in the Domain Generation Algorithms (DGAs) Detection Problem
    (НН ФТІ, КПІ ім. Ігоря Сікорського, 2025) Salyk, Vasyl; Venherskyi, Petro
    This paper investigates the effectiveness of deep learning models (in particular, CNN, LSTM, and DistilBERT) in detecting algorithm-generated domains (DGAs), taking into account the time dynamics of the development of such domains. The models were trained on samples of DGA domains relevant up to and including 2018 and a proportional set of unique benign domains (1:1) and were tested on five annual datasets for the period 2019–2023, which contained annual slices of DGA domains and the corresponding samples of benign domains. The work quantifies the temporal stability of these models and their ability to effectively detect new threats in the context of concept drift. The analysis of the results shows different dynamics of performance indicators for the architectures studied, revealing their strengths and weaknesses in terms of long-term performance and resilience to the evolution of DGA. The findings highlight the critical need to develop strategies to regularly monitor, update, or adapt DGA detection models to ensure a consistently high level of protection in the face of continuous improvement of malicious domain generation techniques. Notably, the findings related to DistilBERT are based on a model trained with a significantly smaller dataset than CNN and LSTM, which limits the validity of direct performance comparisons. This constraint introduces a potential bias in the results and highlights the need for caution when interpreting DistilBERT’s relative performance. A more comprehensive evaluation is underway using an equivalent dataset.
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    Enhancing Salesforce CRM Security Using SIEM Systems
    (НН ФТІ, КПІ ім. Ігоря Сікорського, 2025) Zlatous, Sviatoslav; Venherskyi, Petro
    In today’s cloud-first digital landscape, Customer Relationship Management (CRM) platforms are pivotal for storing, managing, and analyzing customer data. Among these, Salesforce CRM stands out as the industry leader, offering extensive features for customer engagement. However, with this prominence comes a heightened risk of cybersecurity threats. Salesforce processes sensitive data – such as personally identifiable information (PII), financial records, and client communications – which makes it an attractive target for cybercriminals.