2 citations · 2 across the 5 of their papers we have counts for
6 papers · 1 filter
Client-Side Patching against Backdoor Attacks in Federated Learning
Borja Molina-Coronado
Federated learning is a versatile framework for training models in decentralized environments. However, the trust placed in clients makes federated learning vulnerable to backdoor…
Celtibero: Robust Layered Aggregation for Federated Learning
Borja Molina-Coronado
Federated Learning (FL) is an innovative approach to distributed machine learning. While FL offers significant privacy advantages, it also faces security challenges, particularly f…
Light up that Droid! On the Effectiveness of Static Analysis Features against App Obfuscation for Android Malware Detection
Borja Molina-Coronado, Antonio Ruggia, Usue Mori +3
Malware authors have seen obfuscation as the mean to bypass malware detectors based on static analysis features. For Android, several studies have confirmed that many anti-malware…
Efficient Concept Drift Handling for Batch Android Malware Detection Models
Molina-Coronado B., Mori U., Mendiburu A. +1
The rapidly evolving nature of Android apps poses a significant challenge to static batch machine learning algorithms employed in malware detection systems, as they quickly become…
Towards a Fair Comparison and Realistic Evaluation Framework of Android Malware Detectors based on Static Analysis and Machine Learning
Borja Molina-Coronado, Usue Mori, Alexander Mendiburu +1
As in other cybersecurity areas, machine learning (ML) techniques have emerged as a promising solution to detect Android malware. In this sense, many proposals employing a variety…
Survey of Network Intrusion Detection Methods from the Perspective of the Knowledge Discovery in Databases Process
Borja Molina-Coronado, Usue Mori, Alexander Mendiburu +1
The identification of cyberattacks which target information and communication systems has been a focus of the research community for years. Network intrusion detection is a complex…