Journals Indexed in Clarivate Web of Science
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2025
A decision-making framework for user authentication using keystroke dynamics
Abstract
Sophisticated cyber attacks increasingly threaten critical infrastructures, highlighting the need for more reliable user authentication. This work proposes a deep learning–based framework that employs keystroke dynamics and Siamese neural networks to distinguish legitimate users from impostors. To address variability in password lengths, we introduce interpolation-based data fusion strategies that standardize keystroke features across datasets. The proposed framework incorporates adaptive threshold strategies to determine decision boundaries relative to a user’s baseline typing behaviour. Experimental evaluation on fused CMU and KeyRecs datasets achieved equal error rates as low as 0.11–0.12. The results demonstrate the robustness and scalability of the approach for detecting insider threats.
Conference Proceedings
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2026
Evasion of malware classifiers by injecting category-specific benign features
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2025
Targeted evasion of malware detection using adversarial machine learning
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2025
Kenkėjiškų programų aptikimo gerinimas taikant kelių klasių gerybinės programinės įrangos analizę
Santrauka
Šiame darbe siūloma metodika, skirta gerybinės ir kenkėjiškos programinės įrangos kategorizavimui, siekiant padidinti kenkėjiškų programų aptikimo tikslumą. Metodika remiasi statinės analizės duomenimis, derinamais su šiuolaikiniais duomenų apdorojimo ir vizualizacijos metodais.
Conference Abstracts
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2025
AMBER C2: enhancing cyber defence with ethical adversarial machine learning
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2025
Feature level deception or when malware wears a mask
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2025
Improving malware detection by analyzing similarities of multi-category benign software
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2024
Red team tactics against malware detection using adversarial attacks
Abstract
Static and dynamic malware analysis are widely used in cybersecurity, though attackers have adapted to these methods. To improve detection, researchers increasingly combine these methods with machine/deep learning for faster, more efficient malware classification. However, adversaries exploit weaknesses to craft adversarial malware that evades detection. We aim to design a deep learning–based C2 framework to enhance red team training and improve anomaly detection beyond reliance on automated tools.
Other project-related works
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2026
Steganografija dirbtinių neuroninių tinklų parametruose ir jos aptikimas mašininio mokymosi metodais
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2025
Požymių konvertavimo į vaizdus metodų palyginimas kenkėjiškų programų aptikimo efektyvumui gerinti
Publicly available datasets
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2026
WinAPI-AdvMal: A Six-Class Windows API Import Dataset for Adversarial Malware
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2025
WinAPI-4C-AdvMal: Windows API features for adversarial malware