Demo Data from VLDB 2026 / reviewers in the wild / expert
Vishal Mittal
dblp:46/829
· DBLP profile ↗
3ranked-venue papers
1as first author
1since 2021 · last 2021
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Applied, interdisciplinary, general and emerging computing · 2 · 1 since 2021Security and privacy · 1 · 1 first-author
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 |
Network security · 100% | |
| Computer networks
1 paper |
Routing and switching · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Network security › intrusion detection and prevention
intrusion detection |
0.0 | 1 | 2002 | Sensor-based intrusion detection for intra-domain distance-vector routing · CCS 2002 |
Network security › routing security
routing attack detection |
0.0 | 1 | 2002 | Sensor-based intrusion detection for intra-domain distance-vector routing · CCS 2002 |
Routing and switching › routing protocol
distance-vector routing |
0.0 | 1 | 2002 | Sensor-based intrusion detection for intra-domain distance-vector routing · CCS 2002 |
Methods — techniques the papers use, named apart from their topics
sensor signature generation · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2021 | A Three-Fold Machine Learning Approach for Detection of COVID-19 from Audio Data
Vishal Mittal, Yashvardhan Sharma |
ICCSA (3) | 2 |
| 2016 | AcT: Accuracy-aware crawling techniques for cloud-crawler
Kanik Gupta, Vishal Mittal, Bazir Bishnoi, Siddharth Maheshwari, Dhaval Patel 0002 |
World Wide Web | 2 |
| 2002 | Sensor-based intrusion detection for intra-domain distance-vector routingabstractDetection of routing-based attacks is difficult because malicious routing behavior can be identified only in specific network locations. In addition, the configuration of the signatures used by intrusion detection sensors is a time-consuming and error-prone task because it has to take into account both the network topology and the characteristics of the particular routing protocol in use. We describe an intrusion detection technique that uses information about both the network topology and the positioning of sensors to determine what can be considered malicious in a particular place of the network. The technique relies on an algorithm that automatically generates the appropriate sensor signatures. This paper presents a description of the approach, applies it to an intra-domain distance-vector protocol and reports the results of its evaluation. Vishal Mittal, Giovanni Vigna |
CCS | 1 |