Demo Data from EDBT 2026 / reviewers in the wild / expert
Kexin Jiao
dblp:348/3283
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2024
0009-0006-4150-8523ORCID · 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.
| Computer networks
1 paper |
Internet of things and sensor networks · 100% | |
| Network and information security
1 paper |
Network security · 100% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Internet of things and sensor networks › industrial network
fieldbus communication |
0.8 | 1 | 2024 | Intrusion Device Detection in Fieldbus Networks Based on Channel-State Group Fingerprint · IEEE Trans. Inf. Forensics Secur. 2024 |
Internet of things and sensor networks › industrial network › industrial wireless networks
industrial control systems |
0.8 | 1 | 2024 | Intrusion Device Detection in Fieldbus Networks Based on Channel-State Group Fingerprint · IEEE Trans. Inf. Forensics Secur. 2024 |
Network security › intrusion detection and prevention
intrusion detection |
0.8 | 1 | 2024 | Intrusion Device Detection in Fieldbus Networks Based on Channel-State Group Fingerprint · IEEE Trans. Inf. Forensics Secur. 2024 |
Network security › traffic analysis
device fingerprinting |
0.2 | 1 | 2024 | Intrusion Device Detection in Fieldbus Networks Based on Channel-State Group Fingerprint · IEEE Trans. Inf. Forensics Secur. 2024 |
Methods — techniques the papers use, named apart from their topics
collaborative detection · 1.5channel-state group fingerprint · 1.5
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | Intrusion Device Detection in Fieldbus Networks Based on Channel-State Group FingerprintabstractThe rapid development of distributed control technologies has made Fieldbus networks widely used in industrial control systems (ICSs). Meanwhile, the weak security protection of Fieldbus networks exposes potential attack paths for attackers. Attackers can tap covert and unauthorized external devices (i.e., intrusion devices) into the network to launch attacks. As the intrusion device can remain silent when eavesdropping, there is no detectable abnormal traffic in the network to detect the intrusion device. In this paper, we analytically prove that the observed signals sent from any benign device will inevitably change when the intrusion device is tapped into the Fieldbus network. With this knowledge, we construct the channel-state group fingerprint from the communication signals of each benign device and propose a collaborative intrusion detection mechanism for physical access, PhyCID, to passively detect the covert intrusion device. Detection results on a real power distribution cabinet, an RS485 bus testbed, and a controller area network (CAN) bus testbed indicate that PhyCID is purely passive, environmentally adaptive, and protocol-independent in most Fieldbus networks, including RS485 and CAN. Furthermore, extensive experiments under different scenarios demonstrate the effectiveness and robustness of PhyCID. Xiangming Wang, Yang Liu 0090, Kexin Jiao, Xiapu Luo, Ting Liu 0002 |
IEEE Trans. Inf. Forensics Secur. | 3 |