Demo Data from EDBT 2026 / reviewers in the wild / expert
Raphael J. Sofaer
dblp:330/7872
· DBLP profile ↗
2ranked-venue papers
1as first author
2since 2021 · last 2024
0009-0009-7924-6436ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Software engineering, systems software and programming languages · 2 · 1 first-author · 2 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.
| Software engineering, system software, and programming languages
2 papers |
Program analysis · 75% Software maintenance and evolution · 25% | |
| Network and information security
1 paper |
Systems and software security · 100% |
Topics — the 7 heaviest of 7, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Systems and software security
software supply chain security |
0.8 | 1 | 2024 | RogueOne: Detecting Rogue Updates via Differential Data-flow Analysis Using Trust Domains · ICSE 2024 |
Program analysis › static analysis
abstract interpretation |
0.8 | 1 | 2024 | RogueOne: Detecting Rogue Updates via Differential Data-flow Analysis Using Trust Domains · ICSE 2024 |
Program analysis
data flow analysis |
0.8 | 1 | 2024 | RogueOne: Detecting Rogue Updates via Differential Data-flow Analysis Using Trust Domains · ICSE 2024 |
Program analysis
static analysis |
0.8 | 1 | 2024 | RogueOne: Detecting Rogue Updates via Differential Data-flow Analysis Using Trust Domains · ICSE 2024 |
Software maintenance and evolution › software dependencies › software dependency management
dependency update |
0.6 | 1 | 2022 | UPGRADVISOR: Early Adopting Dependency Updates Using Hybrid Program Analysis and Hardware Tracing · OSDI 2022 |
Software maintenance and evolution › software ecosystems
dependency management |
0.2 | 1 | 2024 | RogueOne: Detecting Rogue Updates via Differential Data-flow Analysis Using Trust Domains · ICSE 2024 |
Program analysis
dynamic analysis |
0.2 | 1 | 2022 | UPGRADVISOR: Early Adopting Dependency Updates Using Hybrid Program Analysis and Hardware Tracing · OSDI 2022 |
Methods — techniques the papers use, named apart from their topics
trust domains · 1.5differential data-flow analysis · 1.5abstract interpretation · 1.5program analysis · 0.6hardware tracing · 0.6
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | RogueOne: Detecting Rogue Updates via Differential Data-flow Analysis Using Trust DomainsabstractRogue updates, an important type of software supply-chain attack in which attackers conceal malicious code inside updates to benign software, are a growing problem due to their stealth and effectiveness. We design and implement RogueOne, a system for detecting rogue updates to JavaScript packages. RogueOne uses a novel differential data-flow analysis to capture how an update changes a package's interactions with external APIs. Using an efficient form of abstract interpretation that can exclude unchanged code in a package, it constructs an object data-flow relationship graph (ODRG) that tracks data-flows among objects. RogueOne then maps objects to trust domains, a novel abstraction which summarizes trust relationships in a package. Objects are assigned a trust domain based on whether they originate in the target package, a dependency, or in a system API. RogueOne uses the ODRG to build a set of data-flows across trust domains. It compares data-flow sets across package versions to detect untrustworthy new interactions with external APIs. We evaluated RogueOne on hundreds of npm packages, demonstrating its effectiveness at detecting rogue updates and distinguishing them from benign ones. RogueOne achieves high accuracy and can be more than seven times as effective in detecting rogue updates and avoiding false positives compared to other systems built to detect malicious packages. Raphael J. Sofaer, Yaniv David, Mingqing Kang, Jianjia Yu, Yinzhi Cao, Jason Nieh |
ICSE | 1 |
| 2022 | UPGRADVISOR: Early Adopting Dependency Updates Using Hybrid Program Analysis and Hardware Tracing
Yaniv David, Xudong Sun 0019, Raphael J. Sofaer, Aditya Senthilnathan, Zhiqiang Zuo 0002, Guoqing Harry Xu, Jason Nieh, Ronghui Gu |
OSDI | 3 |