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An automatic updating perceptron-based system for malware detection

Published in 2013 15th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing, 2013

This paper presents an innovative automatic updating perceptron-based system designed for efficient malware detection with adaptive learning capabilities.

Recommended citation: Barat, Marius and Prelipcean, Dumitru Bogdan and Gavrilut, Dragos Teodor, "An automatic updating perceptron-based system for malware detection." 2013 15th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing, pages 303-307, IEEE, 2013. https://doi.org/10.1109/SYNASC.2013.48

A study on common malware families evolution in 2012

Published in Journal of Computer Virology and Hacking Techniques, 2013

A comprehensive analysis of the evolution patterns of major malware families throughout 2012, revealing key trends and adaptation strategies employed by cybercriminals.

Recommended citation: Barat, Marius and Prelipcean, Dumitru-Bogdan and Gavriluț, Dragoș Teodor, "A study on common malware families evolution in 2012." Journal of Computer Virology and Hacking Techniques, vol. 9, no. 4, pages 171-178, Springer Paris, 2013. https://doi.org/10.1007/s11416-013-0194-9

A practical approach on cleaning-up large data sets

Published in 2014 16th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing, 2014

This paper presents practical methodologies for efficiently cleaning and preprocessing large-scale datasets, with applications to cybersecurity data management.

Recommended citation: Barat, Marius and Prelipcean, Dumitru Bogdan and Gavrilut, Dragos Teodor, "A practical approach on cleaning-up large data sets." 2014 16th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing, pages 280-284, IEEE, 2014. https://doi.org/10.1109/SYNASC.2014.46

Improving malware detection response time with behavior-based statistical analysis techniques

Published in 2015 17th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC), 2015

This paper presents novel behavior-based statistical analysis techniques to improve the response time of malware detection systems while maintaining high accuracy rates.

Recommended citation: Prelipcean, Dumitru Bogdan and Popescu, Adrian Stefan and Gavrilut, Dragos Teodor, "Improving malware detection response time with behavior-based statistical analysis techniques." 2015 17th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC), pages 232-239, IEEE, 2015. https://doi.org/10.1109/SYNASC.2015.42

A study on techniques for proactively identifying malicious urls

Published in 2015 17th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC), 2015

This research explores proactive techniques for identifying malicious URLs before they can cause harm, focusing on early detection methods and prevention strategies.

Recommended citation: Popescu, Adrian Stefan and Prelipcean, Dumitru Bogdan and Gavrilut, Dragos Teodor, "A study on techniques for proactively identifying malicious urls." 2015 17th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC), pages 204-211, IEEE, 2015. https://doi.org/10.1109/SYNASC.2015.38

Improving detection of malicious office documents using one-side classifiers

Published in 2019 21st International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC), 2019

This research presents innovative one-side classifier techniques for enhanced detection of malicious Microsoft Office documents, addressing the growing threat of document-based attacks.

Recommended citation: Vitel, Silviu Constantin and Balan, Gheorghe and Prelipcean, Dumitru Bogdan, "Improving detection of malicious office documents using one-side classifiers." 2019 21st International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC), pages 243-247, IEEE, 2019. https://doi.org/10.1109/SYNASC49474.2019.00041

Preventing file-less attacks with machine learning techniques

Published in 2019 21st International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC), 2019

This research develops advanced machine learning techniques specifically designed to detect and prevent sophisticated file-less attacks that evade traditional security measures.

Recommended citation: Bucevschi, Alexandru Gabriel and Balan, Gheorghe and Prelipcean, Dumitru Bogdan, "Preventing file-less attacks with machine learning techniques." 2019 21st International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC), pages 248-252, IEEE, 2019. https://doi.org/10.1109/SYNASC49474.2019.00042

RAMA: a risk assessment solution for healthcare organizations

Published in International Journal of Information Security, 2024

RAMA presents a comprehensive risk assessment framework specifically designed for healthcare organizations, addressing unique cybersecurity challenges in medical environments with patient data protection and operational continuity requirements.

Recommended citation: Smyrlis, Michail and Floros, Evangelos and Basdekis, Ioannis and Prelipcean, Dumitru-Bogdan and Sotiropoulos, Aristeidis and Debar, Herve and Zarras, Apostolis and Spanoudakis, George, "RAMA: a risk assessment solution for healthcare organizations." International Journal of Information Security, vol. 23, no. 3, pages 1821-1838, Springer Berlin Heidelberg, 2024. https://doi.org/10.1007/s10207-024-00826-0

Bridging Threat Models and Detections: Formal Verification via CADP

Published in arXiv preprint arXiv:2509.13035, 2025

This cutting-edge research presents a novel formal verification framework using CADP (Construction and Analysis of Distributed Processes) to bridge the gap between theoretical threat models and practical detection implementations.

Recommended citation: Prelipcean, Dumitru-Bogdan and Dima, Cătălin, "Bridging Threat Models and Detections: Formal Verification via CADP." arXiv preprint arXiv:2509.13035, 2025. https://arxiv.org/abs/2509.13035