77 citations · 171 across the 24 of their papers we have counts for
25 papers
An AI-Based Solution for Secure Service Provisioning in IoT
Marco Arazzi, Mert Cihangiroglu, Serena Nicolazzo +2
As the Internet of Things (IoT) continues its rapid expansion, the attack surface grows accordingly, with emerging threats targeting smart objects and their interactions. In this e…
You Snooze, You Lose: Automatic Safety Alignment Restoration through Neural Weight Translation
Marco Arazzi, Vignesh Kumar Kembu, Antonino Nocera +2
Public repositories now distribute thousands of specialized Low-Rank Adaptation (LoRA) modules, but a third-party adapter routinely arrives without the refusal behavior the same ad…
Security in LLM-as-a-Judge: A Comprehensive SoK
Aiman Al Masoud, Antony Anju, Marco Arazzi +6
LLM-as-a-Judge (LaaJ) is a novel paradigm in which powerful language models are used to assess the quality, safety, or correctness of generated outputs. While this paradigm has sig…
Exploring Structural Complexity in Normative RAG with Graph-based approaches: A case study on the ETSI Standards
Aiman Al Masoud, Marco Arazzi, Simone Germani +1
Industrial standards and normative documents exhibit intricate hierarchical structures, domain-specific lexicons, and extensive cross-referential dependencies, which making it chal…
SecureBreak -- A dataset towards safe and secure models
Marco Arazzi, Vignesh Kumar Kembu, Antonino Nocera
Large language models are becoming pervasive core components in many real-world applications. As a consequence, security alignment represents a critical requirement for their safe…
SD-RAG: A Prompt-Injection-Resilient Framework for Selective Disclosure in Retrieval-Augmented Generation
Aiman Al Masoud, Marco Arazzi, Antonino Nocera
Retrieval-Augmented Generation (RAG) has attracted significant attention due to its ability to combine the generative capabilities of Large Language Models (LLMs) with knowledge ob…
LoRA as Oracle
Marco Arazzi, Antonino Nocera
Practitioners increasingly deploy neural networks they did not train, and must audit them after the fact for hidden backdoors, without the training pipeline, the poisoned data, or…
SoK: The Last Line of Defense: On Backdoor Defense Evaluation
Gorka Abad, Marina Krček, Stefanos Koffas +7
Backdoor attacks pose a significant threat to deep learning models by implanting hidden vulnerabilities that can be activated by malicious inputs. While numerous defenses have been…
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…
DroidTTP: Mapping Android Applications with TTP for Cyber Threat Intelligence
Dincy R Arikkat, Vinod P., Rafidha Rehiman K. A. +4
The widespread adoption of Android devices for sensitive operations like banking and communication has made them prime targets for cyber threats, particularly Advanced Persistent T…
Privacy Preserving and Robust Aggregation for Cross-Silo Federated Learning in Non-IID Settings
Marco Arazzi, Mert Cihangiroglu, Antonino Nocera
Federated Averaging remains the most widely used aggregation strategy in federated learning due to its simplicity and scalability. However, its performance degrades significantly i…
Secure Federated Data Distillation
Marco Arazzi, Mert Cihangiroglu, Serena Nicolazzo +1
Dataset Distillation (DD) is a powerful technique for reducing large datasets into compact, representative synthetic datasets, accelerating Machine Learning training. However, trad…
Augmented Knowledge Graph Querying leveraging LLMs
Marco Arazzi, Davide Ligari, Serena Nicolazzo +1
Adopting Knowledge Graphs (KGs) as a structured, semantic-oriented, data representation model has significantly improved data integration, reasoning, and querying capabilities acro…
Subject Data Auditing via Source Inference Attack in Cross-Silo Federated Learning
Jiaxin Li, Marco Arazzi, Antonino Nocera +1
Source Inference Attack (SIA) in Federated Learning (FL) aims to identify which client used a target data point for local model training. It allows the central server to audit clie…
Let's Focus: Focused Backdoor Attack against Federated Transfer Learning
Marco Arazzi, Stefanos Koffas, Antonino Nocera +1
Federated Transfer Learning (FTL) is the most general variation of Federated Learning. According to this distributed paradigm, a feature learning pre-step is commonly carried out b…
KDk: A Defense Mechanism Against Label Inference Attacks in Vertical Federated Learning
Marco Arazzi, Serena Nicolazzo, Antonino Nocera
Vertical Federated Learning (VFL) is a category of Federated Learning in which models are trained collaboratively among parties with vertically partitioned data. Typically, in a VF…
A Deep Reinforcement Learning Approach for Security-Aware Service Acquisition in IoT
Marco Arazzi, Serena Nicolazzo, Antonino Nocera
The novel Internet of Things (IoT) paradigm is composed of a growing number of heterogeneous smart objects and services that are transforming architectures and applications, increa…
The SemIoE Ontology: A Semantic Model Solution for an IoE-based Industry
Marco Arazzi, Antonino Nocera, Emanuele Storti
Recently, the Industry 5.0 is gaining attention as a novel paradigm, defining the next concrete steps toward more and more intelligent, green-aware and user-centric digital systems…
Privacy-Preserving in Blockchain-based Federated Learning Systems
Sameera K. M., Serena Nicolazzo, Marco Arazzi +4
Federated Learning (FL) has recently arisen as a revolutionary approach to collaborative training Machine Learning models. According to this novel framework, multiple participants…
NLP-Based Techniques for Cyber Threat Intelligence
Marco Arazzi, Dincy R. Arikkat, Serena Nicolazzo +4
In the digital era, threat actors employ sophisticated techniques for which, often, digital traces in the form of textual data are available. Cyber Threat Intelligence~(CTI) is rel…
A Novel IoT Trust Model Leveraging Fully Distributed Behavioral Fingerprinting and Secure Delegation
Marco Arazzi, Serena Nicolazzo, Antonino Nocera
With the number of connected smart devices expected to constantly grow in the next years, Internet of Things (IoT) solutions are experimenting a booming demand to make data collect…
Label Inference Attacks against Node-level Vertical Federated GNNs
Marco Arazzi, Mauro Conti, Stefanos Koffas +4
Federated learning enables collaborative training of machine learning models by keeping the raw data of the involved workers private. Three of its main objectives are to improve th…
Predicting Tweet Engagement with Graph Neural Networks
Marco Arazzi, Marco Cotogni, Antonino Nocera +1
Social Networks represent one of the most important online sources to share content across a world-scale audience. In this context, predicting whether a post will have any impact i…