activity
20202023
most citedCARONTE: Crawling Adversarial Resources Over Non-Trusted, High-Profile Environments

11 citations · 12 across the 3 of their papers we have counts for

collaborators

5 papers

cs.CR20231 cited

You Can Tell a Cybercriminal by the Company they Keep: A Framework to Infer the Relevance of Underground Communities to the Threat Landscape

Michele Campobasso, Radu Rădulescu, Sylvan Brons +1

The criminal underground is populated with forum marketplaces where, allegedly, cybercriminals share and trade knowledge, skills, and cybercrime products. However, it is still uncl…

cs.IR2022

THREAT/crawl: a Trainable, Highly-Reusable, and Extensible Automated Method and Tool to Crawl Criminal Underground Forums

Michele Campobasso, Luca Allodi

Collecting data on underground criminal communities is highly valuable both for security research and security operations. Unfortunately these communities live within a constellati…

cs.CR2020

SAIBERSOC: Synthetic Attack Injection to Benchmark and Evaluate the Performance of Security Operation Centers

Martin Rosso, Michele Campobasso, Ganduulga Gankhuyag +1

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…

cs.CR202011 cited

CARONTE: Crawling Adversarial Resources Over Non-Trusted, High-Profile Environments

Michele Campobasso, Pavlo Burda, Luca Allodi

The monitoring of underground criminal activities is often automated to maximize the data collection and to train ML models to automatically adapt data collection tools to differen…

cs.CR2020

Impersonation-as-a-Service: Characterizing the Emerging Criminal Infrastructure for User Impersonation at Scale

Michele Campobasso, Luca Allodi

In this paper we provide evidence of an emerging criminal infrastructure enabling impersonation attacks at scale. Impersonation-as-a-Service (ImpaaS) allows attackers to systematic…