Mirco Marchetti - IEEE Xplore Author Profile
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Mirco Marchetti

Publications
47
Citations
1,088
Publications by Year
20072024
Co-Authors:
Show All Co-Authors (36)
Image of author Mirco Marchetti

Mirco Marchetti

Affiliation

University of Modena and Reggio Emilia

Publication Topics

Biography

Mirco Marchetti received the Ph.D. degree in information and communication technologies in 2009. He is currently an Associate Professor with the Department of Engineering “Enzo Ferrari,” University of Modena and Reggio Emilia, Italy. His research interests include all aspects of system and network security, security for cyber-physical systems and automotive, cryptography applied to cloud security, and outsourced data and services. More information can be found at https://weblab.ing.unimore.it/people/marchetti.(Based on document published on 19 August 2020).
Publications
47
Citations
1,088
Publications by Year
20072024
Co-Authors:
Show All Co-Authors (36)
Author's Published Works

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Cybersecurity Domains: A design pattern for creating Zero Trust Architectures through microsegmentation

Claudio Zanasi;Mirco Marchetti;Michele Colajanni

2024 IEEE Conference on Dependable, Autonomic and Secure Computing (DASC)
Year: 2024 | Conference Paper |
Perimeter defense strategies are inadequate to ensure cybersecurity of infrastructures consisting of heterogeneous and dynamic resources. The Zero Trust security model emerges as the most promising solution to mitigate risks and protect assets, but significant organizational and implementation challenges hinder its adoption. Microsegmentation of networked systems composed by dynamic IT components ...Show More

Cybersecurity Domains: A design pattern for creating Zero Trust Architectures through microsegmentation

Claudio Zanasi;Mirco Marchetti;Michele Colajanni

Year: 2024 | Conference Paper |

RealCAN: bringing real-time capabilities to canplayer

Giovanni Gambigliani Zoccoli;Dario Stabili;Mirco Marchetti

2024 IEEE 100th Vehicular Technology Conference (VTC2024-Fall)
Year: 2024 | Conference Paper |
In this paper we present RealCAN, a real-time capable extension of the canplayer tool available in can-utils, a collection of utilities for interacting with Controller Area Network bus systems on Linux-based operating systems. In particular, RealCAN addresses the main limitation of working with fixed time intervals while replaying previously collected CAN traces with the canplayer tool, allowing d...Show More

RealCAN: bringing real-time capabilities to canplayer

Giovanni Gambigliani Zoccoli;Dario Stabili;Mirco Marchetti

Year: 2024 | Conference Paper |

HackCar: a test platform for attacks and defenses on a cost-contained automotive architecture

Dario Stabili;Filip Valgimigli;Edoardo Torrini;Mirco Marchetti

2024 IEEE Intelligent Vehicles Symposium (IV)
Year: 2024 | Conference Paper |
Cited by: Papers (1)
In this paper, we introduce the design of HackCar, a testing platform for replicating attacks and defenses on a generic automotive system without requiring access to a complete vehicle. This platform empowers security researchers to illustrate the consequences of attacks targeting an automotive system on a realistic platform, facilitating the development and testing of security countermeasures aga...Show More

HackCar: a test platform for attacks and defenses on a cost-contained automotive architecture

Dario Stabili;Filip Valgimigli;Edoardo Torrini;Mirco Marchetti

Year: 2024 | Conference Paper |

Are VANETs pseudonyms effective? An experimental evaluation of pseudonym tracking in adversarial scenario

Giovanni Gambigliani Zoccoli;Dario Stabili;Mirco Marchetti

2023 IEEE 98th Vehicular Technology Conference (VTC2023-Fall)
Year: 2023 | Conference Paper |
Cited by: Papers (1)
With the increasing adoption of Vehicular Ad Hoc Networks (VANETs) for the development of Cooperative Intelligent Transportation Systems (C-ITS) many concerns regarding privacy and anonymity in VANETs have been raised by security researchers and practitioners, highlighting the need for effective mechanisms to protect sensitive information exchanged by connected vehicles. One of the first concerns ...Show More

Are VANETs pseudonyms effective? An experimental evaluation of pseudonym tracking in adversarial scenario

Giovanni Gambigliani Zoccoli;Dario Stabili;Mirco Marchetti

Year: 2023 | Conference Paper |

Decentralized position detection for moving vehicles

Francesco Pollicino;Samih Eisa;Pedro Rosa;Miguel L. Pardal;Mirco Marchetti

2023 IEEE 97th Vehicular Technology Conference (VTC2023-Spring)
Year: 2023 | Conference Paper |
Cited by: Papers (1)
Modern cars are equipped with sensors that can detect other moving vehicles and obstacles on the road. However, their range is usually limited to line-of-sight and their accuracy is also limited. To provide information beyond the sensor range, each vehicle broadcasts Basic Safety Messages (BSMs) with its position and speed. For road awareness, it would be best if multiple vehicles could confirm th...Show More

Decentralized position detection for moving vehicles

Francesco Pollicino;Samih Eisa;Pedro Rosa;Miguel L. Pardal;Mirco Marchetti

Year: 2023 | Conference Paper |

SixPack v2: enhancing SixPack to avoid last generation misbehavior detectors in VANETs

Giovanni Gambigliani Zoccoli;Francesco Pollicino;Dario Stabili;Mirco Marchetti

2022 IEEE 21st International Symposium on Network Computing and Applications (NCA)
Year: 2022 | Conference Paper |
This paper proposes SixPack v2, an enhanced version of the SixPack attack that allows to evade even state-of-the-art misbehavior detection systems. As the original SixPack, SixPack v2 is a dynamic attack targeting other C-ITS entities by simulating the sudden activation of the braking system with consequent activation of the Anti-lock Braking System. SixPack v2 achieves better evasion by improving...Show More

SixPack v2: enhancing SixPack to avoid last generation misbehavior detectors in VANETs

Giovanni Gambigliani Zoccoli;Francesco Pollicino;Dario Stabili;Mirco Marchetti

Year: 2022 | Conference Paper |

Comparison of Machine Learning-based anomaly detectors for Controller Area Network

Andrea Venturi;Dario Stabili;Francesco Pollicino;Emanuele Bianchi;Mirco Marchetti

2022 IEEE 21st International Symposium on Network Computing and Applications (NCA)
Year: 2022 | Conference Paper |
Cited by: Papers (4)
This paper presents a comparative analysis of different Machine Learning-based detection algorithms designed for Controller Area Network (CAN) communication on three different datasets. This work focuses on addressing the current limitations of related scientific literature, related to the quality of the publicly available datasets and to the lack of public implementations of the detection solutio...Show More

Comparison of Machine Learning-based anomaly detectors for Controller Area Network

Andrea Venturi;Dario Stabili;Francesco Pollicino;Emanuele Bianchi;Mirco Marchetti

Year: 2022 | Conference Paper |

Robustness Evaluation of Network Intrusion Detection Systems based on Sequential Machine Learning

Andrea Venturi;Claudio Zanasi;Mirco Marchetti;Michele Colajanni

2022 IEEE 21st International Symposium on Network Computing and Applications (NCA)
Year: 2022 | Conference Paper |
The rise of sequential Machine Learning (ML) methods has paved the way for a new generation of Network Intrusion Detection Systems (NIDS) which base their classification on the temporal patterns exhibited by malicious traffic. Previous work presents successful algorithms in this field, but just a few attempts try to assess their robustness in real-world contexts. In this paper, we aim to fill this...Show More

Robustness Evaluation of Network Intrusion Detection Systems based on Sequential Machine Learning

Andrea Venturi;Claudio Zanasi;Mirco Marchetti;Michele Colajanni

Year: 2022 | Conference Paper |

Exploring the consequences of cyber attacks on Powertrain Cyber Physical Systems

Dario Stabili;Raffaele Romagnoli;Mirco Marchetti;Bruno Sinopoli;Michele Colajanni

2022 International Conference on Control, Robotics and Informatics (ICCRI)
Year: 2022 | Conference Paper |
Cited by: Papers (3)
This paper proposes a novel approach for the study of cyber-attacks against the powertrain of a generic vehicle. The proposed model is composed of a generic Internal Combustion engine and a speed controller, that communicate through a Controller Area Network (CAN) bus. We consider a threat model composed of three representative attack scenarios designed to modify the output of the model, thus affe...Show More

Exploring the consequences of cyber attacks on Powertrain Cyber Physical Systems

Dario Stabili;Raffaele Romagnoli;Mirco Marchetti;Bruno Sinopoli;Michele Colajanni

Year: 2022 | Conference Paper |

On the effectiveness of BSM communications in V2V emergency scenarios

Francesco Pollicino;Dario Stabili;Mirco Marchetti

2022 IEEE 95th Vehicular Technology Conference: (VTC2022-Spring)
Year: 2022 | Conference Paper |
Cited by: Papers (2)
Cooperative Intelligent Transportation Systems (CITS) improve driving experience and safety through secure Vehicular Ad-hoc NETworks (VANETs) that satisfy strict security and performance constraints. The use of Vehicle-to-Vehicle (V2V) communications to improve safety in emergency scenarios is already considered in the relevant standards. However, there is a lack of scientific efforts to evaluate ...Show More

On the effectiveness of BSM communications in V2V emergency scenarios

Francesco Pollicino;Dario Stabili;Mirco Marchetti

Year: 2022 | Conference Paper |

DAGA: Detecting Attacks to In-Vehicle Networks via N-Gram Analysis

Dario Stabili;Luca Ferretti;Mauro Andreolini;Mirco Marchetti

IEEE Transactions on Vehicular Technology
Year: 2022 | Volume: 71, Issue: 11 | Journal Article |
Cited by: Papers (18)
Recent research showcased several cyber-attacks against unmodified licensed vehicles, demonstrating the vulnerability of their internal networks. Many solutions have already been proposed by industry and academia, aiming to detect and prevent cyber-attacks targeting in-vehicle networks. The majority of these proposals borrow security algorithms and techniques from the classical ICT domain, and in ...Show More

DAGA: Detecting Attacks to In-Vehicle Networks via N-Gram Analysis

Dario Stabili;Luca Ferretti;Mauro Andreolini;Mirco Marchetti

Year: 2022 | Volume: 71, Issue: 11 | Journal Article |

Analysis, prevention and detection of ransomware attacks on Industrial Control Systems

Giorgio Valenziano Santangelo;Vincenzo Giuseppe Colacino;Mirco Marchetti

2021 IEEE 20th International Symposium on Network Computing and Applications (NCA)
Year: 2021 | Conference Paper |
Cited by: Papers (3)
With the advent of Industry 4.0, Industrial Control Systems (ICS) are becoming a prime target for many cyber criminals. We are witnessing a steady increase in the number of ransomware attacks specifically designed to compromise in-dustrial control systems. The consequences of these attacks can be devastating, as they are able to block production processes for days, resulting in a loss of revenue, ...Show More

Analysis, prevention and detection of ransomware attacks on Industrial Control Systems

Giorgio Valenziano Santangelo;Vincenzo Giuseppe Colacino;Mirco Marchetti

Year: 2021 | Conference Paper |

Accountable and privacy-aware flexible car sharing and rental services

Francesco Pollicino;Luca Ferretti;Dario Stabili;Mirco Marchetti

2021 IEEE 20th International Symposium on Network Computing and Applications (NCA)
Year: 2021 | Conference Paper |
The transportation sector is undergoing rapid changes to reduce pollution and increase life quality in urban areas. One of the most effective approaches is flexible car rental and sharing to reduce traffic congestion and parking space issues. In this paper, we envision a flexible car sharing framework where vehicle owners want to make their vehicles available for flexible rental to other users. Th...Show More

Accountable and privacy-aware flexible car sharing and rental services

Francesco Pollicino;Luca Ferretti;Dario Stabili;Mirco Marchetti

Year: 2021 | Conference Paper |

Hardware Limitations to Secure C-ITS: Experimental Evaluation and Solutions

Francesco Pollicino;Dario Stabili;Luca Ferretti;Mirco Marchetti

IEEE Transactions on Vehicular Technology
Year: 2021 | Volume: 70, Issue: 12 | Journal Article |
Cited by: Papers (7)
Cooperative Intelligent Transportation Systems (C-ITS) improve driving experience and safety through secure Vehicular Ad-hoc NETworks (VANETs) that satisfy strict security and performance constraints. Relevant standards, such as the IEEE 1609.2, prescribe network-efficient cryptographic protocols to reduce communication latencies through a combination of the Elliptic Curve Qu-Vanstone (ECQV) impli...Show More

Hardware Limitations to Secure C-ITS: Experimental Evaluation and Solutions

Francesco Pollicino;Dario Stabili;Luca Ferretti;Mirco Marchetti

Year: 2021 | Volume: 70, Issue: 12 | Journal Article |

SixPack: Abusing ABS to avoid Misbehavior detection in VANETs

Francesco Pollicino;Dario Stabili;Giampaolo Bella;Mirco Marchetti

2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring)
Year: 2021 | Conference Paper |
Cited by: Papers (8)
This paper presents SixPack, a cyber attack to VANET communications that is able to go undetected by the current state-of-the-art anomaly detectors. The SixPack attack is a dynamic attack conducted by an insider attacker who modifies the content of the Basic Safety Messages to pretend a sudden activation of the braking system with the consequent activation of the Anti-lock Braking System, and crea...Show More

SixPack: Abusing ABS to avoid Misbehavior detection in VANETs

Francesco Pollicino;Dario Stabili;Giampaolo Bella;Mirco Marchetti

Year: 2021 | Conference Paper |

Glyph: Efficient ML-Based Detection of Heap Spraying Attacks

Fabio Pierazzi;Stefano Cristalli;Danilo Bruschi;Michele Colajanni;Mirco Marchetti;Andrea Lanzi

IEEE Transactions on Information Forensics and Security
Year: 2021 | Volume: 16 | Journal Article |
Cited by: Papers (4)
Heap spraying is probably the most simple and effective memory corruption attack, which fills the memory with malicious payloads and then jumps at a random location in hopes of starting the attacker's routines. To counter this threat, GRAFFITI has been recently proposed as the first OS-agnostic framework for monitoring memory allocations of arbitrary applications at runtime; however, the main cont...Show More

Glyph: Efficient ML-Based Detection of Heap Spraying Attacks

Fabio Pierazzi;Stefano Cristalli;Danilo Bruschi;Michele Colajanni;Mirco Marchetti;Andrea Lanzi

Year: 2021 | Volume: 16 | Journal Article |

An experimental analysis of ECQV implicit certificates performance in VANETs

Francesco Pollicino;Dario Stabili;Luca Ferretti;Mirco Marchetti

2020 IEEE 92nd Vehicular Technology Conference (VTC2020-Fall)
Year: 2020 | Conference Paper |
Cited by: Papers (9)
Emerging Cooperative Intelligent Transportation Systems (C-ITS) enable improved driving experience and safety guarantees, but require secure Vehicular Ad-hoc NETworks (VANETs) that must comply to strict performance constraints. Specialized standards have been defined to these aims, such as the IEEE 1609.2 that uses network-efficient cryptographic protocols to reduce communication latencies. The re...Show More

An experimental analysis of ECQV implicit certificates performance in VANETs

Francesco Pollicino;Dario Stabili;Luca Ferretti;Mirco Marchetti

Year: 2020 | Conference Paper |

Deep Reinforcement Adversarial Learning Against Botnet Evasion Attacks

Giovanni Apruzzese;Mauro Andreolini;Mirco Marchetti;Andrea Venturi;Michele Colajanni

IEEE Transactions on Network and Service Management
Year: 2020 | Volume: 17, Issue: 4 | Journal Article |
Cited by: Papers (71)
As cybersecurity detectors increasingly rely on machine learning mechanisms, attacks to these defenses escalate as well. Supervised classifiers are prone to adversarial evasion, and existing countermeasures suffer from many limitations. Most solutions degrade performance in the absence of adversarial perturbations; they are unable to face novel attack variants; they are applicable only to specific...Show More

Deep Reinforcement Adversarial Learning Against Botnet Evasion Attacks

Giovanni Apruzzese;Mauro Andreolini;Mirco Marchetti;Andrea Venturi;Michele Colajanni

Year: 2020 | Volume: 17, Issue: 4 | Journal Article |

Hardening Random Forest Cyber Detectors Against Adversarial Attacks

Giovanni Apruzzese;Mauro Andreolini;Michele Colajanni;Mirco Marchetti

IEEE Transactions on Emerging Topics in Computational Intelligence
Year: 2020 | Volume: 4, Issue: 4 | Journal Article |
Cited by: Papers (42)
Machine learning algorithms are effective in several applications, but they are not as much successful when applied to intrusion detection in cyber security. Due to the high sensitivity to their training data, cyber detectors based on machine learning are vulnerable to targeted adversarial attacks that involve the perturbation of initial samples. Existing defenses assume unrealistic scenarios; the...Show More

Hardening Random Forest Cyber Detectors Against Adversarial Attacks

Giovanni Apruzzese;Mauro Andreolini;Michele Colajanni;Mirco Marchetti

Year: 2020 | Volume: 4, Issue: 4 | Journal Article |

Detection and Threat Prioritization of Pivoting Attacks in Large Networks

Giovanni Apruzzese;Fabio Pierazzi;Michele Colajanni;Mirco Marchetti

IEEE Transactions on Emerging Topics in Computing
Year: 2020 | Volume: 8, Issue: 2 | Journal Article |
Cited by: Papers (21)
Several advanced cyber attacks adopt the technique of “pivoting” through which attackers create a command propagation tunnel through two or more hosts in order to reach their final target. Identifying such malicious activities is one of the most tough research problems because of several challenges: command propagation is a rare event that cannot be detected through signatures, the huge amount of ...Show More

Detection and Threat Prioritization of Pivoting Attacks in Large Networks

Giovanni Apruzzese;Fabio Pierazzi;Michele Colajanni;Mirco Marchetti

Year: 2020 | Volume: 8, Issue: 2 | Journal Article |

Evaluating the effectiveness of Adversarial Attacks against Botnet Detectors

Giovanni Apruzzese;Michele Colajanni;Mirco Marchetti

2019 IEEE 18th International Symposium on Network Computing and Applications (NCA)
Year: 2019 | Conference Paper |
Cited by: Papers (29)
Classifiers based on Machine Learning are vulnerable to adversarial attacks, which involve the creation of malicious samples that are not classified correctly. While this phenomenon has been extensively studied within the image processing domain, comprehensive analyses are scarce in the cybersecurity field. This is a critical problem because cyber-detectors are being increasingly integrated with m...Show More

Evaluating the effectiveness of Adversarial Attacks against Botnet Detectors

Giovanni Apruzzese;Michele Colajanni;Mirco Marchetti

Year: 2019 | Conference Paper |

Detection of Missing CAN Messages through Inter-Arrival Time Analysis

Dario Stabili;Mirco Marchetti

2019 IEEE 90th Vehicular Technology Conference (VTC2019-Fall)
Year: 2019 | Conference Paper |
Cited by: Papers (13)
Recent cyber-attacks to real vehicles demonstrated the risks related to connected vehicles, and spawned several research effort aimed at proposing algorithms and architectural solutions to improve the security of these vehicles. Most of the documented attacks to the connected vehicles require the injection of maliciously forged messages to subvert the normal behaviour of the electronic microcontro...Show More

Detection of Missing CAN Messages through Inter-Arrival Time Analysis

Dario Stabili;Mirco Marchetti

Year: 2019 | Conference Paper |

Addressing Adversarial Attacks Against Security Systems Based on Machine Learning

Giovanni Apruzzese;Michele Colajanni;Luca Ferretti;Mirco Marchetti

2019 11th International Conference on Cyber Conflict (CyCon)
Year: 2019 | Conference Paper |
Cited by: Papers (63)
Machine-learning solutions are successfully adopted in multiple contexts but the application of these techniques to the cyber security domain is complex and still immature. Among the many open issues that affect security systems based on machine learning, we concentrate on adversarial attacks that aim to affect the detection and prediction capabilities of machine-learning models. We consider reali...Show More

Addressing Adversarial Attacks Against Security Systems Based on Machine Learning

Giovanni Apruzzese;Michele Colajanni;Luca Ferretti;Mirco Marchetti

Year: 2019 | Conference Paper |

READ: Reverse Engineering of Automotive Data Frames

Mirco Marchetti;Dario Stabili

IEEE Transactions on Information Forensics and Security
Year: 2019 | Volume: 14, Issue: 4 | Journal Article |
Cited by: Papers (94)
Security analytics and forensics applied to in-vehicle networks are growing research areas that gained relevance after recent reports of cyber-attacks against unmodified licensed vehicles. However, the application of security analytics algorithms and tools to the automotive domain is hindered by the lack of public specifications about proprietary data exchanged over in-vehicle networks. Since the ...Show More

READ: Reverse Engineering of Automotive Data Frames

Mirco Marchetti;Dario Stabili

Year: 2019 | Volume: 14, Issue: 4 | Journal Article |

Cybersecurity of Connected Autonomous Vehicles : A ranking based approach

Gianfranco Burzio;Giuseppe Faranda Cordella;Michele Colajanni;Mirco Marchetti;Dario Stabili

2018 International Conference of Electrical and Electronic Technologies for Automotive
Year: 2018 | Conference Paper |
Cited by: Papers (6)
The concordant vision of the future automotive landscape foresees vehicles that are always connected to infrastructure and Cloud services, and that are equipped with autonomous driving or advanced driver assistance systems. It is clear that in a similar scenario cybersecurity of modern and future vehicles is paramount. With connected autonomous vehicles the protection from external attack will be ...Show More

Cybersecurity of Connected Autonomous Vehicles : A ranking based approach

Gianfranco Burzio;Giuseppe Faranda Cordella;Michele Colajanni;Mirco Marchetti;Dario Stabili

Year: 2018 | Conference Paper |

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