Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page

Vishal Mittal

dblp:46/829 · DBLP profile ↗
← Back
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

TopicWeightPapersLastEvidence papers
Network security › intrusion detection and prevention
intrusion detection
0.012002
Sensor-based intrusion detection for intra-domain distance-vector routing · CCS 2002
Network security › routing security
routing attack detection
0.012002
Sensor-based intrusion detection for intra-domain distance-vector routing · CCS 2002
Routing and switching › routing protocol
distance-vector routing
0.012002
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
YearPublicationVenuePosition
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 Web2
2002 Sensor-based intrusion detection for intra-domain distance-vector routing
abstract
Detection 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
CCS1