5 papers · 1 filter
Neural Fingerprints for Malware Analysis: An Image-Based Metric Learning Approach with Application to Cross-Domain Classification
Manasa Deshagouni, Sayma Akther, Martin Jurecek +1
Identifying the family of a newly observed malware sample is a core task in threat intelligence, yet conventional classifiers must be retrained whenever a new family appears. This…
Image-Based Techniques and Ensemble Soft Voting for Malware Classification
Sushant Rakesh Lokhande, Fabio Di Troia, Martin Jurecek +1
In this chapter, we investigate image-based malware family classification using an ensemble learning framework and a soft voting strategy. We consider malware binaries that have be…
Building an Adversarial Malware Dataset by Family and Type: Generation, Evasion, and Poisoning Evaluation
David Košťál, Martin Jureček
We present a dataset of adversarial malware samples derived from the public RawMal-TF collection of real-world malware binaries. Using a suite of adversarial malware generators, we…
Gray-Box Poisoning of Continuous Malware Ingestion Pipelines
Jan Dolejš, Martin Jureček, Róbert Lórencz
Modern malware detection pipelines rely on continuous data ingestion and machine learning to counter the high volume of novel threats. This work investigates a realistic gray-box p…
RawMal-TF: Raw Malware Dataset Labeled by Type and Family
David Bálik, Martin Jureček, Mark Stamp
This work addresses the challenge of malware classification using machine learning by developing a novel dataset labeled at both the malware type and family levels. Raw binaries we…