Francesco Marchiori

@unipd.it

Department of Mathematics
University of Padova

Francesco Marchiori


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https://researchid.co/francesco.marchiori
I'm Francesco, a PhD student in Brain, Mind and Computer Science (BMCS) at the University of Padova with a Master's degree in Cybersecurity. Here, I am part of the Security and Privacy (SPRITZ) research group, under the supervision of .

My research interests lie primarily in Automotive Security, with a particular focus on Machine Learning and Deep Learning applications. I am also interested in Adversarial Attacks, Cyber Threat Intelligence, and Quantum Cryptography and Computing.

EDUCATION

M.Sc. Cybersecurity
B.Sc. Information Engineering

RESEARCH, TEACHING, or OTHER INTERESTS

Computer Science, Artificial Intelligence
17

Scopus Publications

306

Scholar Citations

9

Scholar h-index

9

Scholar i10-index

Scopus Publications

  • Inference Attacks on Encrypted Online Voting via Traffic Analysis
    Anastasiia Belousova, Francesco Marchiori, Mauro Conti
    Lecture Notes in Computer Science, 2026
  • DUMB and DUMBer: Is Adversarial Training Worth It in the Real World?
    Francesco Marchiori, Marco Alecci, Luca Pajola, Mauro Conti
    Lecture Notes in Computer Science, 2026
  • Leaky Batteries: A Novel Set of Side-Channel Attacks on Electric Vehicles
    Francesco Marchiori, Mauro Conti
    Lecture Notes in Computer Science, 2025
  • Leaving No Blind Spots: Toward Automotive Cybersecurity
    Francesco Marchiori, Mauro Conti
    Proceedings 2025 55th Annual IEEE IFIP International Conference on Dependable Systems and Networks Supplemental Volume Dsn S 2025, 2025
    The increasing connectivity and autonomy of modern vehicles have drastically expanded their attack surface, introducing interdependent cybersecurity risks. However, existing security mechanisms often focus on isolated threats, failing to address their interplay within complex vehicle ecosystems. As vehicles become increasingly dependent on AI-driven control, electric powertrains, and networked architectures, ensuring resilience across multiple attack vectors requires a holistic security approach. This work proposes a unified three-layer security framework that integrates (i) physical-layer protection through battery authentication and side-channel resilience, (ii) AI-layer robustness against adversarial attacks on perception and intrusion detection, and (iii) communication-layer security for in-vehicle network protection. By leveraging cross-domain security principles, including cyber-physical security analysis, adversarial ML defenses, and in-vehicle network protection, this framework provides a cohesive and scalable methodology for securing next-generation automotive systems.
  • Can LLMs Classify CVEs? Investigating LLMs Capabilities in Computing CVSS Vectors
    Francesco Marchiori, Denis Donadel, Mauro Conti
    Proceedings IEEE Symposium on Computers and Communications, 2025
    Common Vulnerability and Exposure (CVE) records are fundamental to cybersecurity, offering unique identifiers for publicly known software and system vulnerabilities. Each CVE is typically assigned a Common Vulnerability Scoring System (CVSS) score to support risk prioritization and remediation. However, score inconsistencies often arise due to subjective interpretations of certain metrics. As the number of new CVEs continues to grow rapidly, automation is increasingly necessary to ensure timely and consistent scoring. While prior studies have explored automated methods, the application of Large Language Models (LLMs), despite their recent popularity, remains relatively underexplored.In this work, we evaluate the effectiveness of LLMs in generating CVSS scores for newly reported vulnerabilities. We investigate various prompt engineering strategies to enhance their accuracy and compare LLM-generated scores against those from embedding-based models, which use vector representations classified via supervised learning. Our results show that while LLMs demonstrate potential in automating CVSS evaluation, embedding-based methods outperform them in scoring more subjective components, particularly confidentiality, integrity, and availability impacts. These findings underscore the complexity of CVSS scoring and suggest that combining LLMs with embedding-based methods could yield more reliable results across all scoring components.
  • A Survey and Comparative Analysis of Security Properties of CAN Authentication Protocols
    Alessandro Lotto, Francesco Marchiori, Alessandro Brighente, Mauro Conti
    IEEE Communications Surveys and Tutorials, 2025
    Modern cars’ complexity and increased reliance on electronic components have made them a prime target for attackers. In particular, the in-vehicle communication system is one of the major attack surfaces, with the Controller Area Network (CAN) being the most used protocol. CAN connects electronic components with each other, allowing them to communicate and carry out control functions, as well as managing the vehicle state. However, these components, called Electronic Control Units (ECUs), can also be exploited for malicious purposes. Indeed, since the CAN bus was not designed with security features, attackers can exploit its vulnerabilities to compromise ECUs and corrupt the communication, allowing for remote vehicle control, disabling breaks, and engine shutdowns, causing significant safety threats. In response to the absence of standardized authentication protocols within the automotive domain, researchers propose diverse solutions, each with unique strengths and vulnerabilities. However, the continuous influx of new protocols and potential oversights in meeting security requirements and essential operational features further complicate the implementability of these protocols. This paper comprehensively reviews and compares the 15 most prominent authentication protocols for the CAN bus. Our analysis emphasizes their strengths and weaknesses, evaluating their alignment with critical security requirements for automotive authentication. Additionally, we evaluate protocols based on essential operational criteria that contribute to ease of implementation in predefined infrastructures, enhancing overall reliability and reducing the probability of successful attacks. Our study reveals a prevalent focus on defending against external attackers in existing protocols, exposing vulnerabilities to internal threats. Notably, authentication protocols employing hash chains, Mixed Message Authentication Codes, and asymmetric encryption techniques emerge as the most effective approaches. Through our comparative study, we classify the considered protocols based on their security attributes and suitability for implementation, providing valuable insights for future developments in the field.
  • PQ-CAN: A Framework for Simulating Post-Quantum Cryptography in Embedded Systems
    Mauro Conti, Francesco Marchiori, Sebastiano Matarazzo, Marco Rubin
    Proceedings IEEE Symposium on Computers and Communications, 2025
  • Profiling Electric Vehicles via Early Charging Voltage Patterns
    Francesco Marchiori, Denis Donadel, Alessandro Brighente, Mauro Conti
    Lecture Notes in Computer Science, 2025
  • CANEDERLI: On the Impact of Adversarial Training and Transferability on CAN Intrusion Detection Systems
    Francesco Marchiori, Mauro Conti
    Wiseml 2024 Proceedings of the 2024 ACM Workshop on Wireless Security and Machine Learning, 2024
    The growing integration of vehicles with external networks has led to a surge in attacks targeting their Controller Area Network (CAN) internal bus. As a countermeasure, various Intrusion Detection Systems (IDSs) have been suggested in the literature to prevent and mitigate these threats. With the increasing volume of data facilitated by the integration of Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication networks, most of these systems rely on data-driven approaches such as Machine Learning (ML) and Deep Learning (DL) models. However, these systems are susceptible to adversarial evasion attacks. While many researchers have explored this vulnerability, their studies often involve unrealistic assumptions, lack consideration for a realistic threat model, and fail to provide effective solutions.
  • Work-in-Progress: Crash Course: Can (Under Attack) Autonomous Driving Beat Human Drivers?
    Francesco Marchiori, Alessandro Brighente, Mauro Conti
    Proceedings 9th IEEE European Symposium on Security and Privacy Workshops Euro S and Pw 2024, 2024
    Autonomous driving is a research direction that has gained enormous traction in the last few years thanks to advancements in Artificial Intelligence (AI). Depending on the level of independence from the human driver, several studies show that Autonomous Vehicles (AVs) can reduce the number of on-road crashes and decrease overall fuel emissions by improving efficiency. However, security research on this topic is mixed and presents some gaps. On one hand, these studies often neglect the intrinsic vulnerabilities of AI algorithms, which are known to compromise the security of these systems. On the other, the most prevalent attacks towards AI rely on unrealistic assumptions, such as access to the model parameters or the training dataset. As such, it is unclear if autonomous driving can still claim several advantages over human driving in real-world applications. This paper evaluates the inherent risks in autonomous driving by examining the current landscape of AV sand establishing a pragmatic threat model. Through our analysis, we develop specific claims highlighting the delicate balance between the advantages of AVs and potential security challenges in real-world scenarios. Our evaluation serves as a foundation for providing essential takeaway messages, guiding both researchers and practitioners at various stages of the automation pipeline. In doing so, we contribute valuable insights to advance the discourse on the security and viability of autonomous driving in real-world applications.
  • Can LLMs Understand Computer Networks? Towards a Virtual System Administrator
    Denis Donadel, Francesco Marchiori, Luca Pajola, Mauro Conti
    Proceedings Conference on Local Computer Networks LCN, 2024
  • FaultGuard: A Generative Approach to Resilient Fault Prediction in Smart Electrical Grids
    Emad Efatinasab, Francesco Marchiori, Alessandro Brighente, Mirco Rampazzo, Mauro Conti
    Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2024
  • RedactBuster: Entity Type Recognition from Redacted Documents
    Mirco Beltrame, Mauro Conti, Pierpaolo Guglielmin, Francesco Marchiori, Gabriele Orazi
    Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2024
  • Your Battery Is a Blast! Safeguarding Against Counterfeit Batteries with Authentication
    Francesco Marchiori, Mauro Conti
    Ccs 2023 Proceedings of the 2023 ACM Sigsac Conference on Computer and Communications Security, 2023
  • Your Attack Is Too DUMB: Formalizing Attacker Scenarios for Adversarial Transferability
    Marco Alecci, Mauro Conti, Francesco Marchiori, Luca Martinelli, Luca Pajola
    ACM International Conference Proceeding Series, 2023
  • STIXnet: A Novel and Modular Solution for Extracting All STIX Objects in CTI Reports
    Francesco Marchiori, Mauro Conti, Nino Vincenzo Verde
    ACM International Conference Proceeding Series, 2023
  • AGIR: Automating Cyber Threat Intelligence Reporting with Natural Language Generation
    Filippo Perrina, Francesco Marchiori, Mauro Conti, Nino Vincenzo Verde
    Proceedings 2023 IEEE International Conference on Big Data Bigdata 2023, 2023

RECENT SCHOLAR PUBLICATIONS

  • QUACK! Making the (Rubber) Ducky Talk: A Systematic Study of Keystroke Dynamics for HID Injection Detection
    A Lotto, F Marchiori, M Conti
    arXiv preprint arXiv:2604.15845 , 2026
    2026
  • Cybersecurity and AI in Automotive Cyber-Physical Systems
    F Marchiori
    Università degli studi di Padova , 2026
    2026
  • The CTI Echo Chamber: Fragmentation, Overlap, and Vendor Specificity in Twenty Years of Cyber Threat Reporting
    M Suarez-Roman, F Marchiori, M Conti, J Tapiador
    arXiv preprint arXiv:2602.17458 , 2026
    2026
  • Inference Attacks on Encrypted Online Voting via Traffic Analysis
    A Belousova, F Marchiori, M Conti
    International Conference on Information Security, 216-236 , 2025
    2025
    Citations: 1
  • Preventing Robotic Jailbreaking via Multimodal Domain Adaptation
    F Marchiori, R Sinha, C Agia, A Robey, GJ Pappas, M Conti, M Pavone
    arXiv preprint arXiv:2509.23281 , 2025
    2025
    Citations: 1
  • DUMB and DUMBer: Is Adversarial Training Worth It in the Real World?
    F Marchiori, M Alecci, L Pajola, M Conti
    European Symposium on Research in Computer Security, 228-248 , 2025
    2025
    Citations: 4
  • Leaky Batteries: A Novel Set of Side-Channel Attacks on Electric Vehicles
    F Marchiori, M Conti
    International Conference on Availability, Reliability and Security, 322-333 , 2025
    2025
    Citations: 5
  • Profiling Electric Vehicles via Early Charging Voltage Patterns
    F Marchiori, D Donadel, A Brighente, M Conti
    International Conference on Availability, Reliability and Security, 5-22 , 2025
    2025
    Citations: 1
  • PQ-CAN: A Framework for Simulating Post-Quantum Cryptography in Embedded Systems
    M Conti, F Marchiori, S Matarazzo, M Rubin
    2025 IEEE Symposium on Computers and Communications (ISCC), 1-6 , 2025
    2025
    Citations: 4
  • Can LLMs Classify CVEs? Investigating LLMs Capabilities in Computing CVSS Vectors
    F Marchiori, D Donadel, M Conti
    2025 IEEE Symposium on Computers and Communications (ISCC), 1-6 , 2025
    2025
    Citations: 18
  • ATTAQ: Adversarial Robustness of Quantum Machine Learning
    F Marchiori, M Conti
    2025 55th Annual IEEE/IFIP International Conference on Dependable Systems … , 2025
    2025
    Citations: 3
  • Leaving No Blind Spots: Toward Automotive Cybersecurity
    F Marchiori, M Conti
    2025 55th Annual IEEE/IFIP International Conference on Dependable Systems … , 2025
    2025
    Citations: 1
  • Moshi Moshi? A Model Selection Hijacking Adversarial Attack
    R Petrucci, L Pajola, F Marchiori, L Pasa
    arXiv preprint arXiv:2502.14586 , 2025
    2025
  • A Survey and Comparative Analysis of Security Properties of CAN Authentication Protocols
    A Lotto, F Marchiori, A Brighente, M Conti
    IEEE Communications Surveys & Tutorials , 2024
    2024
    Citations: 34
  • Can LLMs Understand Computer Networks? Towards a Virtual System Administrator
    D Donadel, F Marchiori, L Pajola, M Conti
    2024 IEEE 49th Conference on Local Computer Networks (LCN), 1-10 , 2024
    2024
    Citations: 40
  • RedactBuster: Entity Type Recognition from Redacted Documents
    M Beltrame, M Conti, P Guglielmin, F Marchiori, G Orazi
    European Symposium on Research in Computer Security, 451-470 , 2024
    2024
    Citations: 7
  • FaultGuard: A Generative Approach to Resilient Fault Prediction in Smart Electrical Grids
    E Efatinasab, F Marchiori, A Brighente, M Rampazzo, M Conti
    International Conference on Detection of Intrusions and Malware, and … , 2024
    2024
    Citations: 13
  • Work-in-Progress: Crash Course: Can (Under Attack) Autonomous Driving Beat Human Drivers?
    F Marchiori, A Brighente, M Conti
    2024 IEEE European Symposium on Security and Privacy Workshops (EuroS&PW … , 2024
    2024
    Citations: 3
  • CANEDERLI: On The Impact of Adversarial Training and Transferability on CAN Intrusion Detection Systems
    F Marchiori, M Conti
    Proceedings of the 2024 ACM Workshop on Wireless Security and Machine … , 2024
    2024
    Citations: 12
  • AGIR: Automating Cyber Threat Intelligence Reporting with Natural Language Generation
    F Perrina, F Marchiori, M Conti, NV Verde
    2023 IEEE International Conference on Big Data (BigData), 3053-3062 , 2023
    2023
    Citations: 67

MOST CITED SCHOLAR PUBLICATIONS

  • AGIR: Automating Cyber Threat Intelligence Reporting with Natural Language Generation
    F Perrina, F Marchiori, M Conti, NV Verde
    2023 IEEE International Conference on Big Data (BigData), 3053-3062 , 2023
    2023
    Citations: 67
  • STIXnet: A Novel and Modular Solution for Extracting All STIX Objects in CTI Reports
    F Marchiori, M Conti, NV Verde
    Proceedings of the 18th International Conference on Availability … , 2023
    2023
    Citations: 55
  • Can LLMs Understand Computer Networks? Towards a Virtual System Administrator
    D Donadel, F Marchiori, L Pajola, M Conti
    2024 IEEE 49th Conference on Local Computer Networks (LCN), 1-10 , 2024
    2024
    Citations: 40
  • A Survey and Comparative Analysis of Security Properties of CAN Authentication Protocols
    A Lotto, F Marchiori, A Brighente, M Conti
    IEEE Communications Surveys & Tutorials , 2024
    2024
    Citations: 34
  • Can LLMs Classify CVEs? Investigating LLMs Capabilities in Computing CVSS Vectors
    F Marchiori, D Donadel, M Conti
    2025 IEEE Symposium on Computers and Communications (ISCC), 1-6 , 2025
    2025
    Citations: 18
  • Your Attack Is Too DUMB: Formalizing Attacker Scenarios for Adversarial Transferability
    M Alecci, M Conti, F Marchiori, L Martinelli, L Pajola
    Proceedings of the 26th international symposium on research in attacks … , 2023
    2023
    Citations: 18
  • FaultGuard: A Generative Approach to Resilient Fault Prediction in Smart Electrical Grids
    E Efatinasab, F Marchiori, A Brighente, M Rampazzo, M Conti
    International Conference on Detection of Intrusions and Malware, and … , 2024
    2024
    Citations: 13
  • CANEDERLI: On The Impact of Adversarial Training and Transferability on CAN Intrusion Detection Systems
    F Marchiori, M Conti
    Proceedings of the 2024 ACM Workshop on Wireless Security and Machine … , 2024
    2024
    Citations: 12
  • Your Battery Is a Blast! Safeguarding Against Counterfeit Batteries with Authentication
    F Marchiori, M Conti
    Proceedings of the 2023 ACM SIGSAC Conference on Computer and Communications … , 2023
    2023
    Citations: 11
  • When Authentication Is Not Enough: On the Security of Behavioral-Based Driver Authentication Systems
    E Efatinasab, F Marchiori, D Donadel, A Brighente, M Conti
    arXiv preprint arXiv:2306.05923 , 2023
    2023
    Citations: 8
  • RedactBuster: Entity Type Recognition from Redacted Documents
    M Beltrame, M Conti, P Guglielmin, F Marchiori, G Orazi
    European Symposium on Research in Computer Security, 451-470 , 2024
    2024
    Citations: 7
  • Leaky Batteries: A Novel Set of Side-Channel Attacks on Electric Vehicles
    F Marchiori, M Conti
    International Conference on Availability, Reliability and Security, 322-333 , 2025
    2025
    Citations: 5
  • DUMB and DUMBer: Is Adversarial Training Worth It in the Real World?
    F Marchiori, M Alecci, L Pajola, M Conti
    European Symposium on Research in Computer Security, 228-248 , 2025
    2025
    Citations: 4
  • PQ-CAN: A Framework for Simulating Post-Quantum Cryptography in Embedded Systems
    M Conti, F Marchiori, S Matarazzo, M Rubin
    2025 IEEE Symposium on Computers and Communications (ISCC), 1-6 , 2025
    2025
    Citations: 4
  • ATTAQ: Adversarial Robustness of Quantum Machine Learning
    F Marchiori, M Conti
    2025 55th Annual IEEE/IFIP International Conference on Dependable Systems … , 2025
    2025
    Citations: 3
  • Work-in-Progress: Crash Course: Can (Under Attack) Autonomous Driving Beat Human Drivers?
    F Marchiori, A Brighente, M Conti
    2024 IEEE European Symposium on Security and Privacy Workshops (EuroS&PW … , 2024
    2024
    Citations: 3
  • Inference Attacks on Encrypted Online Voting via Traffic Analysis
    A Belousova, F Marchiori, M Conti
    International Conference on Information Security, 216-236 , 2025
    2025
    Citations: 1
  • Preventing Robotic Jailbreaking via Multimodal Domain Adaptation
    F Marchiori, R Sinha, C Agia, A Robey, GJ Pappas, M Conti, M Pavone
    arXiv preprint arXiv:2509.23281 , 2025
    2025
    Citations: 1
  • Profiling Electric Vehicles via Early Charging Voltage Patterns
    F Marchiori, D Donadel, A Brighente, M Conti
    International Conference on Availability, Reliability and Security, 5-22 , 2025
    2025
    Citations: 1
  • Leaving No Blind Spots: Toward Automotive Cybersecurity
    F Marchiori, M Conti
    2025 55th Annual IEEE/IFIP International Conference on Dependable Systems … , 2025
    2025
    Citations: 1