Demo Data from VLDB 2026 / reviewers in the wild / expert
Manjukumar Jayachandra
dblp:49/11041
· DBLP profile ↗
2ranked-venue papers
0as first author
0since 2021 · last 2012
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Systems, architecture and hardware · 1Security and privacy · 1
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% |
Topics — the 1 heaviest of 1, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Malware analysis
dynamic malware analysis |
0.1 | 1 | 2012 | Hubble: Transparent and Extensible Malware Analysis by Combining Hardware Virtualization and Software Emulation · NDSS 2012 |
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2012 | Hubble: Transparent and Extensible Malware Analysis by Combining Hardware Virtualization and Software Emulation
Lok-Kwong Yan, Manjukumar Jayachandra, Mu Zhang 0001, Heng Yin 0001 |
NDSS | 2 |
| 2012 | V2E: combining hardware virtualization and softwareemulation for transparent and extensible malware analysisabstractA transparent and extensible malware analysis platform is essential for defeating malware. This platform should be transparent so malware cannot easily detect and bypass it. It should also be extensible to provide strong support for heavyweight instrumentation and analysis efficiency. However, no existing platform can meet both requirements. Leveraging hardware virtualization technology, analysis platforms like Ether can achieve good transparency, but its instrumentation support and analysis efficiency is poor. In contrast, software emulation provides strong support for code instrumentation and good analysis efficiency by using dynamic binary translation. However, analysis platforms based on software emulation can be easily detected by malware and thus is poor in transparency. To achieve both transparency and extensibility, we propose a new analysis platform that combines hardware virtualization and software emulation. The essence is precise heterogeneous replay: the malware execution is recorded via hardware virtualization and then replayed in software. Our design ensures the execution replay is precise. Moreover, with page-level recording granularity, the platform can easily adjust to analyze various forms of malware (a process, a kernel module, or a shared library). We implemented a prototype called V2E and demonstrated its capability and efficiency by conducting an extensive evaluation with both synthetic samples and 14 realworld emulation-resistant malware samples. Lok-Kwong Yan, Manjukumar Jayachandra, Mu Zhang 0001, Heng Yin 0001 |
VEE | 2 |