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Phishing Detection in IMS Using Domain Ontology and CBA - An Innovative Rule Generation Approach

Authors: Mohammad S. Qaseem; A. Govardhan 0001;

Phishing Detection in IMS Using Domain Ontology and CBA - An Innovative Rule Generation Approach

Abstract

User ignorance towards the use of communication services like Instant Messengers, emails, websites, social networks etc. is becoming the biggest advantage for phishers. It is required to create technical awareness in users by educating them to create a phishing detection application which would generate phishing alerts for the user so that phishing messages are not ignored. The lack of basic security features to detect and prevent phishing has had a profound effect on the IM clients, as they lose their faith in e-banking and e-commerce transactions, which will have a disastrous impact on the corporate and banking sectors and businesses which rely heavily on the internet. Very little research contributions were available in for phishing detection in Instant messengers. A context based, dynamic and intelligent phishing detection methodology in IMs is proposed, to analyze and detect phishing in Instant Messages with relevance to domain ontology (OBIE) and utilizes the Classification based on Association (CBA) for generating phishing rules and alerting the victims. A PDS Monitoring system algorithm is used to identify the phishing activity during exchange of messages in IMs, with high ratio of precision and recall. The results have shown improvement by the increased percentage of precision and recall when compared to the existing methods.

Keywords

FOS: Computer and information sciences, Computer Science - Cryptography and Security, Computer Science - Artificial Intelligence, 68Q99, Computer Science - Information Retrieval, Computer Science - Computers and Society, Artificial Intelligence (cs.AI), Phishing detection, Computers and Society (cs.CY), Ontology based Information Extraction (OBIE),, Instant Messengers, Ontology based Information Extraction (OBIE), Classification based Association (CBA), triplet algorithm, Cryptography and Security (cs.CR), Information Retrieval (cs.IR)

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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