77 citations · 86 across the 17 of their papers we have counts for
7 papers · 1 filter
GShield: Mitigating Poisoning Attacks in Federated Learning
Sameera K. M., Serena Nicolazzo, Antonino Nocera +2
Federated Learning (FL) has recently emerged as a revolutionary approach to collaborative training Machine Learning models. In particular, it enables decentralized model training w…
Protecting Deep Neural Network Intellectual Property with Chaos-Based White-Box Watermarking
Sangeeth B, Serena Nicolazzo, Deepa K. +1
The rapid proliferation of deep neural networks (DNNs) across several domains has led to increasing concerns regarding intellectual property (IP) protection and model misuse. Train…
CTI Dataset Construction from Telegram
Dincy R. Arikkat, Sneha B. T., Serena Nicolazzo +4
Cyber Threat Intelligence (CTI) enables organizations to anticipate, detect, and mitigate evolving cyber threats. Its effectiveness depends on high-quality datasets, which support…
Enhancing Android Malware Detection with Retrieval-Augmented Generation
Saraga S., Anagha M. S., Dincy R. Arikkat +4
The widespread use of Android applications has made them a prime target for cyberattacks, significantly increasing the risk of malware that threatens user privacy, security, and de…
When Forgetting Triggers Backdoors: A Clean Unlearning Attack
Marco Arazzi, Antonino Nocera, Vinod P
Machine unlearning has emerged as a key component in ensuring ``Right to be Forgotten'', enabling the removal of specific data points from trained models. However, even when the un…
XBreaking: Understanding how LLMs security alignment can be broken
Marco Arazzi, Vignesh Kumar Kembu, Antonino Nocera +1
Large Language Models are fundamental actors in the modern IT landscape dominated by AI solutions. However, security threats associated with them might prevent their reliable adopt…
How Secure is Forgetting? Linking Machine Unlearning to Machine Learning Attacks
Muhammed Shafi K. P., Serena Nicolazzo, Antonino Nocera +1
As Machine Learning (ML) evolves, the complexity and sophistication of security threats against this paradigm continue to grow as well, threatening data privacy and model integrity…