1 citations · 1 across the 6 of their papers we have counts for
6 papers
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…
Detecting Concept Drift in Evolving Malware Families Using Rule-Based Classifier Representations
Tomáš Kalný, Martin Jureček, Mark Stamp
This work proposes a structural approach to concept drift detection in malware classification using decision tree rulesets. Classifiers are trained across temporal windows on the E…
A Comparison of Selected Image Transformation Techniques for Malware Classification
Rishit Agrawal, Kunal Bhatnagar, Andrew Do +2
Recently, a considerable amount of malware research has focused on the use of powerful image-based machine learning techniques, which generally yield impressive results. However, b…
Robustness of Selected Learning Models under Label-Flipping Attack
Sarvagya Bhargava, Mark Stamp
In this paper we compare traditional machine learning and deep learning models trained on a malware dataset when subjected to adversarial attack based on label-flipping. Specifical…
Temporal Analysis of Adversarial Attacks in Federated Learning
Rohit Mapakshi, Sayma Akther, Mark Stamp
In this paper, we experimentally analyze the robustness of selected Federated Learning (FL) systems in the presence of adversarial clients. We find that temporal attacks significan…
An Empirical Analysis of Federated Learning Models Subject to Label-Flipping Adversarial Attack
Kunal Bhatnagar, Sagana Chattanathan, Angela Dang +6
In this paper, we empirically analyze adversarial attacks on selected federated learning models. The specific learning models considered are Multinominal Logistic Regression (MLR),…