Zobrazeno 1 - 10
of 10
pro vyhledávání: '"Shahryar Baki"'
Publikováno v:
IEEE Access, Vol 8, Pp 22170-22192 (2020)
We perform an in-depth, systematic benchmarking study and evaluation of phishing features on diverse and extensive datasets. We propose a new taxonomy of features based on the interpretation and purpose of each feature. Next, we propose a benchmarkin
Externí odkaz:
https://doaj.org/article/0486f29d81da4ad7a0ea2e91fbcedaa7
Autor:
Shahryar Baki, Rakesh M. Verma
Publikováno v:
IEEE Transactions on Dependable and Secure Computing. 20:1200-1212
Publikováno v:
IEEE Access, Vol 8, Pp 22170-22192 (2020)
We perform an in-depth, systematic benchmarking study and evaluation of phishing features on diverse and extensive datasets. We propose a new taxonomy of features based on the interpretation and purpose of each feature. Next, we propose a benchmarkin
Publikováno v:
CCS
We describe version 2.0 of our benchmarking framework, PhishBench. With the addition of the ability to dynamically load features, metrics, and classifiers, our new and improved framework allows researchers to rapidly evaluate new features and methods
Publikováno v:
IWSPA@CODASPY
Phishing is a challenging problem that has been addressed by many researchers in several papers using many different datatsets and techniques~\citedas2019sok. Researchers usually test their proposed methods with limited metrics, datasets, and paramet
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 attacks, considerable research has been conducted on these problems along mu
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8fbea47c5e2cf7c622508ae8d2325603
http://arxiv.org/abs/1911.00953
http://arxiv.org/abs/1911.00953
Autor:
Rakesh M. Verma, An Nguyen, Reed Armstrong, Vasanthi Vuppuluri, Ghita Mammar, Arjun Mukherjee, Shahryar Baki
Publikováno v:
Computational Linguistics and Intelligent Text Processing ISBN: 9783319754765
CICLing (1)
CICLing (1)
Automatic collocation recognition has attracted considerable attention of researchers from diverse fields since it is one of the fundamental tasks in NLP, which feeds into several other tasks (e.g., parsing, idioms, summarization, etc.). Despite this
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::462c151270ff10c4b88abeeed93e5f0d
https://doi.org/10.1007/978-3-319-75477-2_11
https://doi.org/10.1007/978-3-319-75477-2_11
Publikováno v:
AsiaCCS
We focus on email-based attacks, a rich field with well-publicized consequences. We show how current Natural Language Generation (NLG) technology allows an attacker to generate masquerade attacks on scale, and study their effectiveness with a within-
Publikováno v:
EACL (Software Demonstrations)
Scopus-Elsevier
Scopus-Elsevier
Collocation and idiom extraction are well-known challenges with many potential applications in Natural Language Processing (NLP). Our experimental, open-source software system, called ICE, is a python package for flexibly extracting collocations and
The CL-SciSumm 2016 shared task introduced an interesting problem: given a document D and a piece of text that cites D, how do we identify the text spans of D being referenced by the piece of text? The shared task provided the first annotated dataset
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b81b28a002663e3ee5170f641f30eb04