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

Ilaria Tavecchia

dblp:145/4106 · DBLP profile ↗
← Back
1ranked-venue papers
0as first author
0since 2021 · last 2014
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Software engineering, systems software and programming languages · 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.

Software engineering, system software, and programming languages
1 paper
Software testing · 50% Software maintenance and evolution · 50%
Network and information security
1 paper
Network security · 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
anomaly detection
0.212014
Checking app behavior against app descriptions · ICSE 2014
Software maintenance and evolution › API usage
API usage analysis
0.212014
Checking app behavior against app descriptions · ICSE 2014
Software testing
mobile application testing
0.212014
Checking app behavior against app descriptions · ICSE 2014

Methods — techniques the papers use, named apart from their topics

outlier detection · 0.4clustering · 0.4
YearPublicationVenuePosition
2014 Checking app behavior against app descriptions
abstract
How do we know a program does what it claims to do? After clustering Android apps by their description topics, we identify outliers in each cluster with respect to their API usage. A "weather" app that sends messages thus becomes an anomaly; likewise, a "messaging" app would typically not be expected to access the current location. Applied on a set of 22,500+ Android applications, our CHABADA prototype identified several anomalies; additionally, it flagged 56% of novel malware as such, without requiring any known malware patterns.
Alessandra Gorla, Ilaria Tavecchia, Florian Gross, Andreas Zeller
ICSE2