EDBT 2026 Demo / reviewers in the wild / expert
Francesco Babboni
dblp:90/7792
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2009
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1Systems, architecture and hardware · 1
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
1 paper |
Cyber-physical and IoT security · 50% Network security · 50% | |
| Computer architecture, parallel and distributed computing, and storage systems
1 paper |
Distributed systems · 100% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Network security › intrusion detection and prevention › intrusion detection › intrusion detection system
distributed intrusion detection |
0.1 | 1 | 2009 | Dynamic distributed intrusion detection for secure multi-robot systems · ICRA 2009 |
Cyber-physical and IoT security › robot security
multi-robot system security |
0.1 | 1 | 2009 | Dynamic distributed intrusion detection for secure multi-robot systems · ICRA 2009 |
Distributed systems
distributed coordination |
0.0 | 1 | 2009 | Dynamic distributed intrusion detection for secure multi-robot systems · ICRA 2009 |
Distributed systems › distributed coordination
multi-agent systems |
0.0 | 1 | 2009 | Dynamic distributed intrusion detection for secure multi-robot systems · ICRA 2009 |
Methods — techniques the papers use, named apart from their topics
state observer · 0.2simulation · 0.2
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2009 | Dynamic distributed intrusion detection for secure multi-robot systemsabstractA general technique to build a dynamic and distributed intrusion detector for a class of multi-agent systems is proposed in this paper, by which misbehavior in the motion of one or more agents can be discovered. Previous work from the authors has focused on how to distinguish the behavior of a misbehaving agent in a completely distributed way, by developing a solution where agents act as local monitors of their neighbors and use locally sensed information as well as data received from other monitors at a particular time. In this work, we improve the system detection capability by allowing monitors to use information collected at different instants and thus realizing a dynamic state observer that is valid for any system in the considered class. Finally, we show through simulations the effectiveness of the proposed solution for a case study. Adriano Fagiolini, Francesco Babboni, Antonio Bicchi |
ICRA | 2 |