activity
20192021
most citedFake Reviews Detection through Ensemble Learning

20 citations · 33 across the 6 of their papers we have counts for

collaborators

8 papers

cs.HC2021

Toward Explainable Users: Using NLP to Enable AI to Understand Users' Perceptions of Cyber Attacks

Faranak Abri, Luis Felipe Gutierrez, Chaitra T. Kulkarni +2

To understand how end-users conceptualize consequences of cyber security attacks, we performed a card sorting study, a well-known technique in Cognitive Sciences, where participant…

cs.CR20204 cited

Phishing Detection through Email Embeddings

Luis Felipe Gutiérrez, Faranak Abri, Miriam Armstrong +2

The problem of detecting phishing emails through machine learning techniques has been discussed extensively in the literature. Conventional and state-of-the-art machine learning al…

cs.SD2020

Predicting Emotions Perceived from Sounds

Faranak Abri, Luis Felipe Gutiérrez, Akbar Siami Namin +2

Sonification is the science of communication of data and events to users through sounds. Auditory icons, earcons, and speech are the common auditory display schemes utilized in son…

cs.CL2020

Fake Reviews Detection through Analysis of Linguistic Features

Faranak Abri, Luis Felipe Gutierrez, Akbar Siami Namin +2

Online reviews play an integral part for success or failure of businesses. Prior to purchasing services or goods, customers first review the online comments submitted by previous c…

cs.CR2020

Cloud as an Attack Platform

Moitrayee Chatterjee, Prerit Datta, Faranak Abri +2

We present an exploratory study of responses from security professionals and ethical hackers in order to understand how they abuse cloud platforms for attack purposes. The par…

cs.LG202020 cited

Fake Reviews Detection through Ensemble Learning

Luis Gutierrez-Espinoza, Faranak Abri, Akbar Siami Namin +2

Customers represent their satisfactions of consuming products by sharing their experiences through the utilization of online reviews. Several machine learning-based approaches can…