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Ketki Sane

dblp:340/0464 · DBLP profile ↗
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1ranked-venue papers
1as first author
1since 2021 · last 2023
0000-0002-5269-5647ORCID · reported

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

Software engineering, systems software and programming languages · 1 · 1 first-author · 1 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.

Artificial intelligence
1 paper
Knowledge representation and reasoning · 100%
Databases, data mining, and information retrieval
1 paper
Information retrieval · 100%
Network and information security
1 paper
Network security · 100%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Knowledge, reasoning and agents › Knowledge representation and reasoning › logic in computer science › logical foundations › non-classical logics › epistemic logic
modal logic
0.712023
Semantically Rich Framework to Automate Cyber Insurance Services · IEEE Trans. Serv. Comput. 2023
Knowledge, reasoning and agents › Knowledge representation and reasoning › ontology
semantic web
0.712023
Semantically Rich Framework to Automate Cyber Insurance Services · IEEE Trans. Serv. Comput. 2023
Information retrieval
text analysis
0.712023
Semantically Rich Framework to Automate Cyber Insurance Services · IEEE Trans. Serv. Comput. 2023
Network security › security economics
cyber insurance
0.212023
Semantically Rich Framework to Automate Cyber Insurance Services · IEEE Trans. Serv. Comput. 2023

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

semantic web · 2.0modal logic · 2.0information retrieval · 2.0
YearPublicationVenuePosition
2023 Semantically Rich Framework to Automate Cyber Insurance Services
abstract
With the rapid enhancements in technology and the adoption of web services, there has been a significant increase in cyber threats faced by organizations in cyberspace. It has become essential to get financial cover to mitigate the expenses due to a security incident. Organizations want to purchase adequate cyber insurance to safeguard against the third-party services they use. However, cyber insurance policies describe their coverages and exclusions using legal jargon that can be difficult to comprehend. Parsing these policy documents and extracting the rules embedded in them is currently a very manual time-consuming process. We have developed a novel framework that automatically extracts the coverage and exclusion key terms and rules embedded in a cyber policy. We have built our framework using Information Retrieval and Artificial Intelligence techniques, specifically Semantic Web and Modal Logic. We have also developed a web interface where users can find the best matching cyber insurance policy based on particular coverage criteria. To validate our approach, we used industry standards proposed by the Federal Trade Commission document (FTC) and have applied it against publicly available policies of seven insurance providers. Our system will allow cyber insurance seekers to explore various policy documents and compare the paradigms mentioned in those documents while selecting the best relevant policy documents.
Ketki Sane, Karuna P. Joshi, Sudip Mittal
IEEE Trans. Serv. Comput.1