66 citations · 161 across the 18 of their papers we have counts for
19 papers
Towards Automated Cyber Threat Intelligence Elicitation in Underground Forums
Lorenzo Bossi, Federico Saccani, Francesco Panebianco +4
Cyber threat intelligence from underground forums has traditionally relied on passive monitoring. However, as users have become more aware of large-scale data collection, valuable…
On the (In)Security of Loading Machine Learning Models
Gabriele Digregorio, Marco Di Gennaro, Stefano Zanero +2
The rise of model sharing through frameworks and dedicated hubs makes Machine Learning significantly more accessible. Despite its benefits, loading shared models exposes users to u…
LeakSealer: A Semisupervised Defense for LLMs Against Prompt Injection and Leakage Attacks
Francesco Panebianco, Stefano Bonfanti, Francesco Trovò +1
The generalization capabilities of Large Language Models (LLMs) have led to their widespread deployment across various applications. However, this increased adoption has introduced…
TimberStrike: Dataset Reconstruction Attack Revealing Privacy Leakage in Federated Tree-Based Systems
Marco Di Gennaro, Giovanni De Lucia, Stefano Longari +2
Federated Learning has emerged as a privacy-oriented alternative to centralized Machine Learning, enabling collaborative model training without direct data sharing. While extensive…
GOLIATH: A Decentralized Framework for Data Collection in Intelligent Transportation Systems
Davide Maffiola, Stefano Longari, Michele Carminati +2
Intelligent Transportation Systems (ITSs) technology has advanced during the past years, and it is now used for several applications that require vehicles to exchange real-time dat…
CyFence: Securing Cyber-Physical Controllers via Trusted Execution Environment
Stefano Longari, Alessandro Pozone, Jessica Leoni +4
In the last decades, Cyber-physical Systems (CPSs) have experienced a significant technological evolution and increased connectivity, at the cost of greater exposure to cyber-attac…
Assessing the Resilience of Automotive Intrusion Detection Systems to Adversarial Manipulation
Stefano Longari, Paolo Cerracchio, Michele Carminati +1
The security of modern vehicles has become increasingly important, with the controller area network (CAN) bus serving as a critical communication backbone for various Electronic Co…
Evaluating the Impact of Privacy-Preserving Federated Learning on CAN Intrusion Detection
Gabriele Digregorio, Elisabetta Cainazzo, Stefano Longari +2
The challenges derived from the data-intensive nature of machine learning in conjunction with technologies that enable novel paradigms such as V2X and the potential offered by 5G c…
How stealthy is stealthy? Studying the Efficacy of Black-Box Adversarial Attacks in the Real World
Francesco Panebianco, Mario D'Onghia, Stefano Zanero aand Michele Carminati
Deep learning systems, critical in domains like autonomous vehicles, are vulnerable to adversarial examples (crafted inputs designed to mislead classifiers). This study investigate…
Poster: FedBlockParadox -- A Framework for Simulating and Securing Decentralized Federated Learning
Gabriele Digregorio, Francesco Bleggi, Federico Caroli +3
A significant body of research in decentralized federated learning focuses on combining the privacy-preserving properties of federated learning with the resilience and transparency…
Tarallo: Evading Behavioral Malware Detectors in the Problem Space
Gabriele Digregorio, Salvatore Maccarrone, Mario D'Onghia +4
Machine learning algorithms can effectively classify malware through dynamic behavior but are susceptible to adversarial attacks. Existing attacks, however, often fail to find an e…
PackHero: A Scalable Graph-based Approach for Efficient Packer Identification
Marco Di Gennaro, Mario D'Onghia, Mario Polino +2
Anti-analysis techniques, particularly packing, challenge malware analysts, making packer identification fundamental. Existing packer identifiers have significant limitations: sign…
Amatriciana: Exploiting Temporal GNNs for Robust and Efficient Money Laundering Detection
Marco Di Gennaro, Francesco Panebianco, Marco Pianta +2
Money laundering is a financial crime that poses a serious threat to financial integrity and social security. The growing number of transactions makes it necessary to use automatic…
Evaluating the Potential of Quantum Machine Learning in Cybersecurity: A Case-Study on PCA-based Intrusion Detection Systems
Armando Bellante, Tommaso Fioravanti, Michele Carminati +2
Quantum computing promises to revolutionize our understanding of the limits of computation, and its implications in cryptography have long been evident. Today, cryptographers are a…
An Anomaly Detection System Based on Generative Classifiers for Controller Area Network
Chunheng Zhao, Stefano Longari, Michele Carminati +1
As electronic systems become increasingly complex and prevalent in modern vehicles, securing onboard networks is crucial, particularly as many of these systems are safety-critical.…
The Illusion of Randomness: An Empirical Analysis of Address Space Layout Randomization Implementations
Lorenzo Binosi, Gregorio Barzasi, Michele Carminati +2
Address Space Layout Randomization (ASLR) is a crucial defense mechanism employed by modern operating systems to mitigate exploitation by randomizing processes' memory layouts. How…
CANflict: Exploiting Peripheral Conflicts for Data-Link Layer Attacks on Automotive Networks
Alvise de Faveri Tron, Stefano Longari, Michele Carminati +2
Current research in the automotive domain has proven the limitations of the CAN protocol from a security standpoint. Application-layer attacks, which involve the creation of malici…
CANdito: Improving Payload-based Detection of Attacks on Controller Area Networks
Stefano Longari, Alessandro Nichelini, Carlo Alberto Pozzoli +2
Over the years, the increasingly complex and interconnected vehicles raised the need for effective and efficient Intrusion Detection Systems against on-board networks. In light of…
Constrained Concealment Attacks against Reconstruction-based Anomaly Detectors in Industrial Control Systems
Alessandro Erba, Riccardo Taormina, Stefano Galelli +4
Recently, reconstruction-based anomaly detection was proposed as an effective technique to detect attacks in dynamic industrial control networks. Unlike classical network anomaly d…