Online citations, reference lists, and bibliographies.

What Phishing E-mails Reveal: An Exploratory Analysis Of Phishing Attempts Using Text Analysis

D. O'Leary
Published 2019 · Computer Science

Cite This
Download PDF
Analyze on Scholarcy
Share
ABSTRACT Accountants and auditing firms are frequent phishing targets because of the proximity to organizational resources. Since phishing typically is done using emails, text analysis is used to e...
This paper references
10.1109/IAW.2006.1652125
Analysis and Defensive Tools for Social-Engineering Attacks on Computer Systems
L. Laribee (2006)
10.1080/01638530701739181
On Lying and Being Lied To: A Linguistic Analysis of Deception in Computer-Mediated Communication
J. Hancock (2007)
10.1016/j.future.2016.02.018
Spam filtering framework for multimodal mobile communication based on dendritic cell algorithm
El-Sayed M. El-Alfy (2016)
10.1140/epjds/s13688-016-0085-1
SentiBench - a benchmark comparison of state-of-the-practice sentiment analysis methods
Filipe Nunes Ribeiro (2016)
10.1002/(SICI)1099-1174(199809)7:3<187::AID-ISAF144>3.0.CO;2-7
Using neural networks to predict corporate failure
Daniel E. O’Leary (1998)
10.1016/j.dss.2011.03.002
Why do people get phished? Testing individual differences in phishing vulnerability within an integrated, information processing model
A. Vishwanath (2011)
10.3233/HSM-1987-7103
The organizational impact of expert systems
D. O'Leary (1987)
10.4304/JSW.2.3.43-55
Spam Email Classification using an Adaptive Ontology
Seongwook Youn (2007)
10.1177/0261927X09351676
The Psychological Meaning of Words: LIWC and Computerized Text Analysis Methods
Y. Tausczik (2010)
10.1080/07421222.2016.1205927
Untangling a Web of Lies: Exploring Automated Detection of Deception in Computer-Mediated Communication
S. Ludwig (2016)
10.1109/eCrime.2012.6489521
PhishAri: Automatic realtime phishing detection on twitter
A. Aggarwal (2012)
10.1109/MIS.2000.1227234
Different Firms, Different Ontologies, and No One Best Ontology
D. O'Leary (2000)
10.1007/978-3-319-89722-6_8
Design, Formal Specification and Analysis of Multi-Factor Authentication Solutions with a Single Sign-On Experience
Giada Sciarretta (2018)
10.1007/s10588-005-5379-y
Structure in the Enron Email Dataset
P. Keila (2005)
10.1016/j.dss.2016.05.006
On the relationship between number of votes and sentiment in crowdsourcing ideas and comments for innovation: A case study of Canada's digital compass
Daniel E. O'Leary (2016)
10.2979/ESERVICEJ.9.1.106
Psychosocial Modeling of Insider Threat Risk Based on Behavioral and Word Use Analysis
F. L. Greitzer (2013)
10.1109/ICDCS.2016.10
Know Your Phish: Novel Techniques for Detecting Phishing Sites and Their Targets
S. Marchal (2016)
10.1111/j.1540-5915.1993.tb00480.x
Determining Differences in Expert Judgment: Implications for Knowledge Acquisition and Validation*
D. O'Leary (1993)
10.1007/S40558-015-0035-Y
Analyzing user reviews in tourism with topic models
M. Rossetti (2016)
Assessing Quality of Consumer Reviews in Mobile Application Markets: A Principal Component Analysis Approach
Sangwook Ha (2015)
10.1007/11507840_9
Modeling and Preventing Phishing Attacks
M. Jakobsson (2005)
10.4135/9781412952606.n172
Federal Trade Commission (FTC)
George Norman (2014)
10.1109/ECRIME.2008.4696967
Evolutionary study of phishing
D. Irani (2008)
Python 3 text processing with NLTK 3 cookbook : over 80 practical recipes on natural language processing techniques using Python's NLTK 3.0
Jacob Perkins (2014)
10.1108/OIR-04-2015-0106
Individual processing of phishing emails: How attention and elaboration protect against phishing
Brynne Harrison (2016)
10.1002/isaf.1386
Natural Language Processing in Accounting, Auditing and Finance: A Synthesis of the Literature with a Roadmap for Future Research
Ingrid E. Fisher (2016)
10.1109/IAW.2007.381908
Do Word Clues Suffice in Detecting Spai and Phishing?
N.C. Rowe (2007)



This paper is referenced by
Semantic Scholar Logo Some data provided by SemanticScholar