Demo Data from EDBT 2026 / reviewers in the wild / expert
Arpit Gogia
dblp:215/3646
· DBLP profile ↗
2ranked-venue papers
0as first author
1since 2021 · last 2025
0009-0006-0949-0888ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Systems, architecture and hardware · 1 · 1 since 2021Software engineering, systems software and programming languages · 1 · 1 since 2021Databases, data management, data science and information retrieval · 1Applied, interdisciplinary, general and emerging computing · 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
2 papers |
Hardware security and side channels · 95% Web and mobile security · 5% | |
| Computer architecture, parallel and distributed computing, and storage systems
1 paper |
Electronic design automation · 77% Processor architecture and microarchitecture · 23% | |
| Artificial intelligence
1 paper |
Graph learning · 100% | |
| Databases, data mining, and information retrieval
1 paper |
Data mining · 100% |
Topics — the 7 heaviest of 7, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Hardware security and side channels › microarchitectural attacks › transient execution attack › speculative execution attack
spectre defense |
0.9 | 1 | 2025 | AMuLeT: Automated Design-Time Testing of Secure Speculation Countermeasures · ASPLOS (2) 2025 |
Hardware security and side channels › microarchitectural attacks › transient execution attack
speculative execution attack |
0.9 | 1 | 2025 | AMuLeT: Automated Design-Time Testing of Secure Speculation Countermeasures · ASPLOS (2) 2025 |
Electronic design automation
hardware verification and test |
0.9 | 1 | 2025 | AMuLeT: Automated Design-Time Testing of Secure Speculation Countermeasures · ASPLOS (2) 2025 |
Machine learning › Graph learning
graph neural network |
0.3 | 1 | 2018 | Collective Classification of Spam Campaigners on Twitter: A Hierarchical Meta-Path Based Approach · WWW 2018 |
Data mining › anomaly detection
spam detection |
0.3 | 1 | 2018 | Collective Classification of Spam Campaigners on Twitter: A Hierarchical Meta-Path Based Approach · WWW 2018 |
Processor architecture and microarchitecture › speculative execution
secure speculation |
0.3 | 1 | 2025 | AMuLeT: Automated Design-Time Testing of Secure Speculation Countermeasures · ASPLOS (2) 2025 |
Web and mobile security
online social network abuse |
0.1 | 1 | 2018 | Collective Classification of Spam Campaigners on Twitter: A Hierarchical Meta-Path Based Approach · WWW 2018 |
Methods — techniques the papers use, named apart from their topics
automated design-time testing · 1.7meta-path · 1.0collective classification · 1.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | AMuLeT: Automated Design-Time Testing of Secure Speculation CountermeasuresabstractIn recent years, several hardware-based countermeasures proposed to mitigate Spectre attacks have been shown to be insecure. To enable the development of effective secure speculation countermeasures, we need easy-to-use tools that can automatically test their security guarantees early-on in the design phase to facilitate rapid prototyping. Leo Tenenbaum, David Adler, Assaf Klein, Arpit Gogia, Alaa R. Alameldeen, Marco Guarnieri, Mark Silberstein, Oleksii Oleksenko, Gururaj Saileshwar |
ASPLOS (2) | 5 |
| 2018 | Collective Classification of Spam Campaigners on Twitter: A Hierarchical Meta-Path Based ApproachabstractCybercriminals have leveraged the popularity of a large user base available on Online Social Networks~(OSNs) to spread spam campaigns by propagating phishing URLs, attaching malicious contents, etc. However, another kind of spam attacks using phone numbers has recently become prevalent on OSNs, where spammers advertise phone numbers to attract users» attention and convince them to make a call to these phone numbers. The dynamics of phone number based spam is different from URL-based spam due to an inherent trust associated with a phone number. While previous work has proposed strategies to mitigate URL-based spam attacks, phone number based spam attacks have received less attention. Srishti Gupta 0003, Abhinav Khattar, Arpit Gogia, Ponnurangam Kumaraguru, Tanmoy Chakraborty 0002 |
WWW | 3 |