A Survey of Phishing Email Filtering Techniques

Almomani, Ammar; Gupta, Brij B.; Atawneh, Samer; Meulenberg, A.; Almomani, Eman

doi:10.1109/surv.2013.030713.00020

Cited by 206 publications

(109 citation statements)

References 46 publications

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“…Unsupervised learning is used which is faster but having low accuracy and hybrid learning which is time consuming and costly. [3] In this paper Ou Wu and Weiming Hu have done work to filter sensitive text by combining semantics and statistics features and analyzed them to construct the CNN (cellular neural network) like word net. This can help to extract right clues, text and helps to avoiding blocking normal text.…”

Section: Research Work Conducted On Web Content Filteringmentioning

confidence: 99%

Web Content Filtration Using Different Web Mining Techniques in Educational System: An Overview

Modi¹,

Jagtap²

2017

IJARCSSE

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Abstract-Internet is widely used as ICT tool in today's education. The web content filtering is essential tool used to filter offensive, unwanted web content from web pages. Internet is widely used in educational organisations as an ICT tool. To protect our tech savvy student from accessing adult sites, offensive, unwanted data in absence of teachers and parents in school as well as home. A strong filter is required to prevent this type of unsolicited activity. In this paper we review the existing filter tools and their techniques.

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Section: Research Work Conducted On Web Content Filteringmentioning

confidence: 99%

Web Content Filtration Using Different Web Mining Techniques in Educational System: An Overview

Modi¹,

Jagtap²

2017

IJARCSSE

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“…"Where: NP→P, NL→P, NP→L, NL→L denote the number of correctly labeled phishing instances, the number of wrongly labeled legitimate instances, the number of phishing instances that are incorrectly recognized as legitimate, and the number of legitimate instances that are identified correctly as legitimate respectively [1,3]." …”

Section: Accuracymentioning

confidence: 99%

“…Many efforts have been made to overcome the phishing attacks through numerous phishing detecting approaches. Nevertheless, phishing has caused enormous money loss in the cyberspace over the past years, which has motivated researchers to seek effective phishing detection techniques that protect users' digital identity [1][2][3]. In general, phishing detection techniques fall into several categories due to the deployed scenarios of detection.…”

Section: Introductionmentioning

confidence: 99%

“…In contrary, classification techniques involved those relied on machine learning classifiers and data mining based scenarios. They differ in terms of classification accuracies, rates of classification errors, and demands on external resources [1][2][3][4][5]. However, they commonly have deployed features as the key factor for classification task, such as hybrid features.…”

Section: Introductionmentioning

confidence: 99%

“…emails and websites) and deploy them to distinguish phish instances from the legitimate ones [1][2][3][4][5]. Thus, classification techniques outperformed their competitors by intuitively detecting phishing that exploits the web to protect clients [3,6]. Moreover, they could automatically extract features from webpage content; URL of websites, hosting information, and classifying their phishness [7 and 8].…”

Section: Introductionmentioning

confidence: 99%

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The Effect of Feature Selection on Phish Website Detection

Zuhair¹,

Selmat²,

Salleh³

2015

ijacsa

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Abstract-Recently, limited anti-phishing campaigns have given phishers more possibilities to bypass through their advanced deceptions. Moreover, failure to devise appropriate classification techniques to effectively identify these deceptions has degraded the detection of phishing websites. Consequently, exploiting as new; few; predictive; and effective features as possible has emerged as a key challenge to keep the detection resilient. Thus, some prior works had been carried out to investigate and apply certain selected methods to develop their own classification techniques. However, no study had generally agreed on which feature selection method that could be employed as the best assistant to enhance the classification performance. Hence, this study empirically examined these methods and their effects on classification performance. Furthermore, it recommends some promoting criteria to assess their outcomes and offers contribution on the problem at hand. Hybrid features, low and high dimensional datasets, different feature selection methods, and classification models were examined in this study. As a result, the findings displayed notably improved detection precision with low latency, as well as noteworthy gains in robustness and prediction susceptibilities. Although selecting an ideal feature subset was a challenging task, the findings retrieved from this study had provided the most advantageous feature subset as possible for robust selection and effective classification in the phishing detection domain.

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