Michele Campobasso

dblp:249/1159 · DBLP profile ↗
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4ranked-venue papers
2as first author
2since 2021 · last 2025
0000-0002-5247-7711ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Security and privacy · 4 · 2 first-author · 2 since 2021
YearPublicationVenuePosition
2025 Cross-Jurisdictional Compliance with Privacy Laws: How Websites Adapt Consent Notices to Regional Regulations
Xander Smeets, Michele Campobasso, Nicola Zannone
ARES (1)2
2023 Know Your Cybercriminal: Evaluating Attacker Preferences by Measuring Profile Sales on an Active, Leading Criminal Market for User Impersonation at Scale
Michele Campobasso, Luca Allodi
USENIX Security Symposium1
2020 SAIBERSOC: Synthetic Attack Injection to Benchmark and Evaluate the Performance of Security Operation Centers
abstract
In this paper we introduce SAIBERSOC, a tool and methodology enabling security researchers and operators to evaluate the performance of deployed and operational Security Operation Centers (SOCs) (or any other security monitoring infrastructure). The methodology relies on the MITRE ATT&CK Framework to define a procedure to generate and automatically inject synthetic attacks in an operational SOC to evaluate any output metric of interest (e.g., detection accuracy, time-to-investigation, etc.). To evaluate the effectiveness of the proposed methodology, we devise an experiment with n = 124 students playing the role of SOC analysts. The experiment relies on a real SOC infrastructure and assigns students to either a BADSOC or a GOODSOC experimental condition. Our results show that the proposed methodology is effective in identifying variations in SOC performance caused by (minimal) changes in SOC configuration. We release the SAIBERSOC tool implementation as free and open source software.
Martin Rosso, Michele Campobasso, Ganduulga Gankhuyag, Luca Allodi
ACSAC2
2020 Impersonation-as-a-Service: Characterizing the Emerging Criminal Infrastructure for User Impersonation at Scale
abstract
In this paper we provide evidence of an emerging criminal infrastructure enabling impersonation attacks at scale. Impersonation-as-a-Service (IMPaaS) allows attackers to systematically collect and enforce user profiles (consisting of user credentials, cookies, device and behavioural fingerprints, and other metadata) to circumvent risk-based authentication system and effectively bypass multi-factor authentication mechanisms. We present the IMPaaS model and evaluate its implementation by analysing the operation of a large, invite-only, Russian IMPaaS platform providing user profiles for more than 260,000 Internet users worldwide. Our findings suggest that the IMPaaS model is growing, and provides the mechanisms needed to systematically evade authentication controls across multiple platforms, while providing attackers with a reliable, up-to-date, and semi-automated environment enabling target selection and user impersonation against Internet users as scale.
Michele Campobasso, Luca Allodi
CCS1