Detection of Phishing Attacks: A Machine Learning Approach
Ram B. Basnet,Srinivas Mukkamala,Andrew H. Sung +2 more
- pp 373-383
TLDR
Though there are several anti-phishing software and techniques for detecting potential phishing attempts in emails and detecting phishing contents on websites, phishers come up with new and hybrid techniques to circumvent the availableSoftware and techniques.Abstract:
Phishing is a form of identity theft that occurs when a malicious Web site impersonates a legitimate one in order to acquire sensitive information such as passwords, account details, or credit card numbers.Though there are several anti-phishing software and techniques for detecting potential phishing attempts in emails and detecting phishing contents on websites, phishers come up with new and hybrid techniques to circumvent the available software and techniques.read more
Citations
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The Case for Learned Index Structures
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A Survey of Phishing Email Filtering Techniques
TL;DR: This is the first comprehensive survey to discuss methods of protection against phishing email attacks in detail, and presents an overview of the various techniques presently used to detect phishing emails, at the different stages of attack, mostly focusing on machine-learning techniques.
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Textual and Visual Content-Based Anti-Phishing: A Bayesian Approach
TL;DR: A novel framework using a Bayesian approach for content-based phishing web page detection is presented, which takes into account textual and visual contents to measure the similarity between the protected web page and suspicious web pages.
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A survey of the applications of text mining in financial domain
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A Taxonomy of Attacks and a Survey of Defence Mechanisms for Semantic Social Engineering Attacks
Ryan Heartfield,George Loukas +1 more
TL;DR: A taxonomy of semantic attacks, as well as a survey of applicable defences, is presented, contrasting the threat landscape and the associated mitigation techniques in a single comparative matrix to identify the areas where further research can be particularly beneficial.
References
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Data Mining: Practical Machine Learning Tools and Techniques
TL;DR: This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining.
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Data Mining
TL;DR: In this paper, generalized estimating equations (GEE) with computing using PROC GENMOD in SAS and multilevel analysis of clustered binary data using generalized linear mixed-effects models with PROC LOGISTIC are discussed.
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Original Contribution: A scaled conjugate gradient algorithm for fast supervised learning
TL;DR: Experiments show that SCG is considerably faster than BP, CGL, and BFGS, and avoids a time consuming line search.