A multi-tier phishing detection and filtering approach

Islam, Rafiqul; Abawajy, Jemal

doi:10.1016/j.jnca.2012.05.009

Cited by 102 publications

(84 citation statements)

References 31 publications

(77 reference statements)

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“…The system proposed by Guo et al [5] assessed the severity of phishing attacks in terms of the risk levels and the potential loss in market value suffered by the targeted firms. The supervised classification techniques are used, which is a major stream of data mining, to assess the severity of phishing attacks.…”

Section: Assessing the Severity Of Phishing Attacks: A Hybrid Data MImentioning

confidence: 99%

“…At the same time, the key antecedents that contribute to a high risk level or a high financial loss generation by a phishing attack are identified. Guo et al [5] used a hybrid approach which combines key phrase extraction and supervised classification methods that make use of the textual data description of the phishing attack with the financial data of the targeted company to assess the severity of a phishing attack according to its risk level or financial loss generating potential. The neural network (NN), decision tree (DT), and support vector machine (SVM) are used to classify the risk levels.…”

Section: Assessing the Severity Of Phishing Attacks: A Hybrid Data MImentioning

confidence: 99%

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Improving Phishing URL Detection Using Fuzzy Association Mining

Nivedha¹,

Gokulan²,

Karthik³

et al. 2017

THE IJES

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Section: Assessing the Severity Of Phishing Attacks: A Hybrid Data MImentioning

confidence: 99%

Section: Assessing the Severity Of Phishing Attacks: A Hybrid Data MImentioning

confidence: 99%

Improving Phishing URL Detection Using Fuzzy Association Mining

Nivedha¹,

Gokulan²,

Karthik³

et al. 2017

THE IJES

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“…Islam and Abawajy [22] propose a multi-tier phishing detection and filtering approach for phishing email filtering. They also propose a method for extracting the features of phishing email based on weighting of message content and message header and select the features according to priority ranking.…”

Section: Related Workmentioning

confidence: 99%

“…According to Islam and Abawajy [22], "phishing attacks continue to pose serious risks for consumers and businesses as well as threatening global security and the economy". This calls for the development of effective countermeasures against email-born phishing attacks in order to safeguard critical infrastructures such as banking Phishing is a type of semantic attack in which victims are sent emails that deceive them into providing sensitive information such as account numbers, passwords, or other personal to phisher.…”

Section: Introductionmentioning

confidence: 99%

Using Feature Selection and Classification Scheme for Automating Phishing Email Detection

Hamid¹,

Abawajy²,

KIM³

2013

SIC

Self Cite

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Email has become the critical communication medium for most organizations. Unfortunately, email-born attacks in computer networks are causing considerable economic losses worldwide. Exiting phishing email blocking appliances have little effect in weeding out the vast majority of phishing emails. At the same time, online criminals are becoming more dangerous and sophisticated. Phishing emails are more active than ever before and putting the average computer user and organizations at risk of significant data, brand and financial loss. In this paper, we propose a hybrid feature selection approach based on combination of content-based and behaviour-based. The approach could mine the attacker behaviour based on email header. On a publicly available test corpus, our hybrid features selection is able to achieve 94% accuracy rate.

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“…From study we proposed a method for Online Fraud Transaction prevention using EVC and QR code techniques. R. Islam et al (2013) [8] proposed a new approach called multi-tier classification model for phishing email filtering. They also propose an innovative method for extracting the features of phishing email based on weighting of message content and message header and select the features according to priority ranking.…”

Section: Introductionmentioning

confidence: 99%

An Ensemble Model for Identification of Phishing Website

Virdi¹

2017

IJRASET

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Identification of phishing websites is very challenging task for every internet and e-mail users. To protect the information from unauthorized person classification of phishing websites is very important. In this research work , we have used many data mining based classification techniques like C4.5, SimpleCart, Random tree, SVM and MLP for classification of phishing websites with different data partitions like 75% training and 25% testing,, 80% training and 20% testing and 85% training and 15% testing. To develop a robust model , we have ensemble the models with different combinations. We have achieved better accuracy with ensemble of C4.5, SimpleCart , MLP and Random tree with all data partitions, but it achieved best accuracy as 97.16% in case of 85-15% data partition.

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A multi-tier phishing detection and filtering approach

Cited by 102 publications

References 31 publications

Improving Phishing URL Detection Using Fuzzy Association Mining

Improving Phishing URL Detection Using Fuzzy Association Mining

Using Feature Selection and Classification Scheme for Automating Phishing Email Detection

An Ensemble Model for Identification of Phishing Website

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