Demo Data from EDBT 2026 / reviewers in the wild / expert
Seok Min Hong
dblp:63/6842
· DBLP profile ↗
2ranked-venue papers
1as first author
1since 2021 · last 2024
—ORCID · conflict
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1Security 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 |
Malware analysis · 100% | |
| Computer architecture, parallel and distributed computing, and storage systems
1 paper |
Cloud and datacenter computing · 100% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Malware analysis › ransomware
ransomware detection |
0.8 | 1 | 2024 | Poster: Detecting Ransomware Attacks by Analyzing Replicated Block Snapshots Using Neural Networks · CCS 2024 |
Cloud and datacenter computing › virtualization
virtual machine |
0.8 | 1 | 2024 | Poster: Detecting Ransomware Attacks by Analyzing Replicated Block Snapshots Using Neural Networks · CCS 2024 |
Methods — techniques the papers use, named apart from their topics
neural network · 1.5deep learning · 1.5
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | Poster: Detecting Ransomware Attacks by Analyzing Replicated Block Snapshots Using Neural NetworksabstractCloud antivirus solutions address limitations of host-based malware detection such as extensive resource consumption. However, they remain vulnerable to sophisticated polymorphic and privileged malware. Also, existing solutions are not suitable to defend against destructive ransomware attacks. We propose an enhancement to existing cloud antivirus solutions that enables deep learning-based block snapshot analysis to detect evasive and privileged ransomware in virtualized environment without requiring any hardware support. Preliminary results validate the proposed approach. Seok Min Hong, Beom Heyn Kim, Mohammad Mannan |
CCS | 1 |
| 2020 | Automatic detection of tympanic membrane and middle ear infection from oto-endoscopic images via convolutional neural networks
Mohammad Azam Khan, Soonwook Kwon, Jaegul Choo, Seok Min Hong, Sung Hun Kang, Il-Ho Park, Sung Kyun Kim, Seok Jin Hong |
Neural Networks | 4 |