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Zhaozhou Tang

dblp:392/3152 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Cyber-physical and IoT security › in-vehicle network security › controller area network security
CAN bus intrusion detection
0.812024
ERACAN: Defending Against an Emerging CAN Threat Model · CCS 2024
Cyber-physical and IoT security › in-vehicle network security
controller area network security
0.812024
ERACAN: Defending Against an Emerging CAN Threat Model · CCS 2024
Network security › intrusion detection and prevention
intrusion detection
0.812024
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
YearPublicationVenuePosition
2024 ERACAN: Defending Against an Emerging CAN Threat Model
abstract
The 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 Notably, recent works show that this model is outdated and that a more potent and versatile model could soon become the norm, prompting the need for a new defense paradigm.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
CCS1