Demo Data from VLDB 2026 / reviewers in the wild / expert
Rohit Aich
dblp:385/7014
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2024
—ORCID · none
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 |
Systems and software security · 100% | |
| Software engineering, system software, and programming languages
1 paper |
Operating systems · 100% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Systems and software security
auditing |
0.8 | 1 | 2024 | eAudit: A Fast, Scalable and Deployable Audit Data Collection System · SP 2024 |
Operating systems › kernel
linux kernel |
0.2 | 1 | 2024 | eAudit: A Fast, Scalable and Deployable Audit Data Collection System · SP 2024 |
Methods — techniques the papers use, named apart from their topics
kernel tracing · 1.5eBPF · 1.5
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | eAudit: A Fast, Scalable and Deployable Audit Data Collection SystemabstractToday’s advanced cyber attack campaigns can often bypass all existing protections. The primary defense against them is after-the-fact detection, followed by a forensic analysis to understand their impact. Such an analysis requires audit logs (also called provenance logs) that faithfully capture all activities and data flows on each host. While the Linux auditing daemon (auditd) and sysdig are the most popular tools for audit data collection, a number of other systems, authored by researchers and practitioners, are also available. Through a motivating experimental study, we show that these systems impose high overheads, slowing workloads by 2× to 8×; lose a majority of events under sustained workloads; and are vulnerable to log tampering that erases log entries before they are committed to persistent storage. We present a new approach that overcomes these challenges. By relying on the extended Berkeley Packet Filter (eBPF) framework built into recent Linux versions, we avoid changes to the kernel code, and hence our data collector works out of the box on most Linux distributions. We present new design, tuning and optimization techniques that enables our system to sustain workloads that are an order of magnitude more intense than those causing major data loss with existing systems. Moreover, our system incurs only a fraction of the overhead of previous systems, while considerably reducing data volumes, and shrinking the log tampering window by ~ 100×. R. Sekar 0001, Hanke Kimm, Rohit Aich |
SP | 3 |