Demo Data from VLDB 2026 / reviewers in the wild / expert
Claudio Oechsler
dblp:335/5212
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2022
0000-0002-6335-811XORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Security and privacy · 1 · 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 |
Network security · 67% Cyber-physical and IoT security · 33% | |
| Computer architecture, parallel and distributed computing, and storage systems
1 paper |
Embedded and real-time 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 |
0.6 | 1 | 2022 | Asymmetric Symbol and Skew Sender Identification for Automotive Networks · IEEE Trans. Inf. Forensics Secur. 2022 |
Network security › protocol security
source authentication |
0.6 | 1 | 2022 | Asymmetric Symbol and Skew Sender Identification for Automotive Networks · IEEE Trans. Inf. Forensics Secur. 2022 |
Cyber-physical and IoT security
vehicular network security |
0.6 | 1 | 2022 | Asymmetric Symbol and Skew Sender Identification for Automotive Networks · IEEE Trans. Inf. Forensics Secur. 2022 |
Embedded and real-time systems › automotive embedded systems
electronic control unit |
0.2 | 1 | 2022 | Asymmetric Symbol and Skew Sender Identification for Automotive Networks · IEEE Trans. Inf. Forensics Secur. 2022 |
Methods — techniques the papers use, named apart from their topics
voltage-based intrusion detection · 1.1time-based classification · 1.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2022 | Asymmetric Symbol and Skew Sender Identification for Automotive NetworksabstractOver recent years, many vulnerabilities have been exposed in vehicles, whose probability of occurrence will rise in the future due to connectivity and increasing system complexity. Even more serious that the Controller Area Network (CAN), widely used by Electronic Control Units (ECUs) to exchange safety-critical messages within a vehicle, does not provide any security measures. In this context, Intrusion Detection Systems (IDSs) have recently been proposed that use time characteristics in the analog transmission signal to identify the sending ECU and detect unauthorized messages. However, realistic application of proposed approaches is not yet tangible, with the full potential of time-based systems yet to be determined. In this elaboration, we therefore establish a foundation by working out robust time characteristics first and analyze root causes for their variation between ECUs. Combining the results with insights from related research, we deduce properties which help to design reliable and feasible IDSs for CAN. Finally, we emphasize these properties by presenting ASSASSIN, an IDS that uses time characteristics to identify the sender of a CAN message and assess authenticity. Achieving an average detection rate of 99.02 %, real-time capable classification and an adaptability to temperature fluctuations, we demonstrate the potential of time-based IDSs on a prototype setup and a real vehicle using resource-limited hardware, also contrasting them with well-elaborated voltage-based IDSs. Oleg Schell, Claudio Oechsler, Marcel Kneib |
IEEE Trans. Inf. Forensics Secur. | 2 |