Phishing detection based Associative Classification data mining

Abdelhamid, Neda; Ayesh, Aladdin; Thabtah, Fadi

doi:10.1016/j.eswa.2014.03.019

Cited by 291 publications

(192 citation statements)

References 14 publications

Supporting

Mentioning

162

Contrasting

Unclassified

Order By: Relevance

“…They suggested that 'request URL', 'age of domain' and 'HTTPS and SSL' are the most significant features, while 'Disabling Right Click' and '@ in the URL' are the lowest significant features. Actually, '@ in the URL' never attends in legitimate web pages, and some literatures took it as significant feature for phishing detection [8], [11], [21]. Basnet et al [22] investigated correlation-based feature selection (CFS) and wrapper feature selection (WFS) techniques for phishing web pages detection.…”

Section: Feature Engineering For Phishing Web Pages Detectionmentioning

confidence: 99%

Application of Feature Engineering for Phishing Detection

Zhang

Ren

Jiang

2016

IEICE Trans. Inf. & Syst.

View full text Add to dashboard Cite

SUMMARYPhishing attacks target financial returns by luring Internet users to exposure their sensitive information. Phishing originates from e-mail fraud, and recently it is also spread by social networks and short message service (SMS), which makes phishing become more widespread. Phishing attacks have drawn great attention due to their high volume and causing heavy losses, and many methods have been developed to fight against them. However, most of researches suffered low detection accuracy or high false positive (FP) rate, and phishing attacks are facing the Internet users continuously. In this paper, we are concerned about feature engineering for improving the classification performance on phishing web pages detection. We propose a novel anti-phishing framework that employs feature engineering including feature selection and feature extraction. First, we perform feature selection based on genetic algorithm (GA) to divide features into critical features and non-critical features. Then, the non-critical features are projected to a new feature by implementing feature extraction based on a two-stage projection pursuit (PP) algorithm. Finally, we take the critical features and the new feature as input data to construct the detection model. Our anti-phishing framework does not simply eliminate the non-critical features, but considers utilizing their projection in the process of classification, which is different from literatures. Experimental results show that the proposed framework is effective in detecting phishing web pages.

show abstract

Section: Feature Engineering For Phishing Web Pages Detectionmentioning

confidence: 99%

Application of Feature Engineering for Phishing Detection

Zhang

Ren

Jiang

2016

IEICE Trans. Inf. & Syst.

View full text Add to dashboard Cite

show abstract

“…CANTINA, A Content-based approach proposed by Hong et al [1] to detect the phishing websites based on the TF-IDF information retrieval algorithm. In this paper the design and evaluation of several heuristics are also discussed.…”

Section: Cantina: a Content-based Approach To Detecting Phishing Web mentioning

confidence: 99%

“…This experiments show that CANTINA is good at detecting phishing sites, correctly labeling approximately 95% of phishing sites. Hong et al [1] presented the design, implementation, and evaluation of CANTINA, 1 a novel content-based approach for detecting phishing web sites. CANTINA examines the content of a web page to determine whether it is legitimate or not, in contrast to other approaches that look at surface characteristics of a web page, for example the URL and its domain name.…”

Section: Cantina: a Content-based Approach To Detecting Phishing Web mentioning

confidence: 99%

Improving Phishing URL Detection Using Fuzzy Association Mining

Nivedha¹,

Gokulan²,

Karthik³

et al. 2017

THE IJES

View full text Add to dashboard Cite

show abstract

“…The websites were compared with these 4 categories to be classified into one of them. Association classification was also used to develop a data mining approach and a rule discovery method was adopted to develop classification systems [24]. According to the results, there are rules for classifying a site into the group of phishing sites or normal ones.…”

Section: Related Workmentioning

confidence: 99%

Proposing a new clustering method to detect phishing websites

Arab¹,

Sohrabi²

2017

Turk J Elec Eng & Comp Sci

View full text Add to dashboard Cite

Abstract:Phishing websites are fake ones that are developed by ill-intentioned people to imitate real and legal websites.Most of these types of web pages have high visual similarities to hustle the victims. The victims of phishing websites may give their bank accounts, passwords, credit card numbers, and other important information to the designers and owners of phishing websites. The increasing number of phishing websites has become a great challenge in e-business in general and in electronic banking specifically. In the present study, a novel framework based on model-based clustering is introduced to fight against phishing websites. First, a model is developed out of those websites that already have been identified as phishing websites as well as real websites that belong to the original owners. Then each new website is compared with the model and categorized into one of the model clusters by a probability. The analyses reveal that the proposed algorithm has high accuracy.

show abstract

Phishing detection based Associative Classification data mining

Cited by 291 publications

References 14 publications

Application of Feature Engineering for Phishing Detection

Application of Feature Engineering for Phishing Detection

Improving Phishing URL Detection Using Fuzzy Association Mining

Proposing a new clustering method to detect phishing websites

Contact Info

Product

Resources

About