activity
20002021
most citedIdentifying Reference Spans: Topic Modeling and Word Embeddings help IR

6 citations · 15 across the 6 of their papers we have counts for

collaborators

11 papers

cs.LG2021

Claim Verification using a Multi-GAN based Model

Amartya Hatua, Arjun Mukherjee, Rakesh M. Verma

This article describes research on claim verification carried out using a multiple GAN-based model. The proposed model consists of three pairs of generators and discriminators. The…

cs.CL2020

Modeling Coherency in Generated Emails by Leveraging Deep Neural Learners

Avisha Das, Rakesh M. Verma

Advanced machine learning and natural language techniques enable attackers to launch sophisticated and targeted social engineering-based attacks. To counter the active attacker iss…

cs.CR20202 cited

Less is More: Exploiting Social Trust to Increase the Effectiveness of a Deception Attack

Shahryar Baki, Rakesh M. Verma, Arjun Mukherjee +1

Cyber attacks such as phishing, IRS scams, etc., still are successful in fooling Internet users. Users are the last line of defense against these attacks since attackers seem to al…

cs.CR20192 cited

SOK: A Comprehensive Reexamination of Phishing Research from the Security Perspective

Avisha Das, Shahryar Baki, Ayman El Aassal +2

Phishing and spear-phishing are typical examples of masquerade attacks since trust is built up through impersonation for the attack to succeed. Given the prevalence of these attack…

cs.IR2019

Online News Media Website Ranking Using User Generated Content

Samaneh Karimi, Azadeh Shakery, Rakesh Verma

News media websites are important online resources that have drawn great attention of text mining researchers. The main aim of this study is to propose a framework for ranking onli…

cs.CL2019

Automated email Generation for Targeted Attacks using Natural Language

Avisha Das, Rakesh Verma

With an increasing number of malicious attacks, the number of people and organizations falling prey to social engineering attacks is proliferating. Despite considerable research in…