Demo Data from EDBT 2026 / reviewers in the wild / expert
Zhaozhou Tang
dblp:392/3152
· DBLP profile ↗
1ranked-venue papers
1as first author
1since 2021 · last 2024
0009-0000-1196-5041ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Security and privacy · 1 · 1 first-author · 1 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
1 paper |
Cyber-physical and IoT security · 67% Network security · 33% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Cyber-physical and IoT security › in-vehicle network security › controller area network security
CAN bus intrusion detection |
0.8 | 1 | 2024 | ERACAN: Defending Against an Emerging CAN Threat Model · CCS 2024 |
Cyber-physical and IoT security › in-vehicle network security
controller area network security |
0.8 | 1 | 2024 | ERACAN: Defending Against an Emerging CAN Threat Model · CCS 2024 |
Network security › intrusion detection and prevention
intrusion detection |
0.8 | 1 | 2024 | ERACAN: Defending Against an Emerging CAN Threat Model · CCS 2024 |
Methods — techniques the papers use, named apart from their topics
threat analysis · 0.8testbed evaluation · 0.8
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | ERACAN: Defending Against an Emerging CAN Threat ModelabstractThe Controller Area Network (CAN) is a pivotal communication protocol extensively utilized in vehicles, aircraft, factories, and diverse cyber-physical systems (CPSs). The extensive CAN security literature resulting from decades of wide usage may create an impression of thorough scrutiny. However, a closer look reveals its reliance on a specific threat model with a limited range of abilities. Notably, recent works show that this model is outdated and that a more potent and versatile model could soon become the norm, prompting the need for a new defense paradigm. Unfortunately, the security impact of this emerging model on CAN systems has not received sufficient attention, and the defense systems addressing it are almost nonexistent. In this paper, we introduce ERACAN, the first comprehensive defense system against this new threat model. We first begin with a threat analysis to ensure that ERACAN comprehensively understands this model's capabilities, evasion tactics, and propensity to enable new attacks or enhance existing ones. ERACAN offers versatile protection against this spectrum of threats, providing attack detection, classification, and optional prevention abilities. We implement and evaluate ERACAN on a testbed and a real vehicle's CAN bus to demonstrate its low latency, real-time operation, and protective capabilities. ERACAN achieves detection rates of 100% and 99.7%+ for all attacks launched by the conventional and the enhanced threat models, respectively. Zhaozhou Tang, Khaled Serag, Saman A. Zonouz, Z. Berkay Celik, Dongyan Xu, Raheem A. Beyah |
CCS | 1 |