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Carlotta Tagliaro

dblp:342/4320 · DBLP profile ↗
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3ranked-venue papers
2as first author
3since 2021 · last 2024
0009-0003-0095-4525ORCID · corroborated

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

Security and privacy · 3 · 2 first-author · 3 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Network and information security
2 papers
Privacy and data protection · 62% Cyber-physical and IoT security · 19% Network security · 19%
Computer networks
1 paper
Internet of things and sensor networks · 100%

Topics — the 2 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Cyber-physical and IoT security › iot device security
iot vulnerability analysis
0.212023
IoTFlow: Inferring IoT Device Behavior at Scale through Static Mobile Companion App Analysis · CCS 2023
Network security
traffic analysis
0.212023
I Still Know What You Watched Last Sunday: Privacy of the HbbTV Protocol in the European Smart TV Landscape · NDSS 2023

Methods — techniques the papers use, named apart from their topics

static analysis · 1.3
YearPublicationVenuePosition
2024 Large-Scale Security Analysis of Real-World Backend Deployments Speaking IoT-Focused Protocols
abstract
Internet-of-Things (IoT) devices, ranging from smart home assistants to health devices, are pervasive: Forecasts estimate their number to reach 29 billion by 2030. Understanding the security of their machine-to-machine communication is crucial. Prior work focused on identifying devices’ vulnerabilities or proposed protocol-specific solutions. Instead, we investigate the security of backends speaking IoT protocols, that is, the backbone of the IoT ecosystem.
Carlotta Tagliaro, Martina Komsic, Andrea Continella, Kevin Borgolte, Martina Lindorfer
RAID1
2023 IoTFlow: Inferring IoT Device Behavior at Scale through Static Mobile Companion App Analysis
abstract
The number of "smart'' devices, that is, devices making up the Internet of Things (IoT), is steadily growing. They suffer from vulnerabilities just as other software and hardware. Automated analysis techniques can detect and address weaknesses before attackers can misuse them. Applying existing techniques or developing new approaches that are sufficiently general is challenging though. Contrary to other platforms, the IoT ecosystem features various software and hardware architectures.
Carlotta Tagliaro, Kevin Borgolte, Martina Lindorfer
CCS2
2023 I Still Know What You Watched Last Sunday: Privacy of the HbbTV Protocol in the European Smart TV Landscape
Carlotta Tagliaro, Florian Hahn 0001, Riccardo Sepe, Alessio Aceti, Martina Lindorfer
NDSS1