Learn to Detect Phishing Scams Using Learning and Ensemble ?Methods

Saberi,; Vahidi,; Bidgoli,

doi:10.1109/wiiatw.2007.4427596

Cited by 9 publications

(9 citation statements)

References 6 publications

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“…This technique achieved a recall level of 100% and a precision that was slightly higher than that of C5.0 (although not significantly so). Saberi et al [18] used a classifier ensemble to detect phishing scams. The ensemble employs a simple consensus technique in order to combine the results of the K-NN, poisson probability distributions and naïve bayes algorithms in order to boost performance of the system.…”

Section: Related Workmentioning

confidence: 99%

Feature selection for Spam and Phishing detection

Toolan

Carthy

2010

2010 eCrime Researchers Summit

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Unsolicited Bulk Email (UBE) has become a large problem in recent years. The number of mass mailers in existence is increasing dramatically. Automatically detecting UBE has become a vital area of current research. Many email clients (such as Outlook and Thunderbird) already have junk filters built in. Mass mailers are continually evolving and overcoming some of the junk filters. This means that the need for research in the area is ongoing. Many existing techniques seem to randomly choose the features that will be used for classification. This paper aims to address this issue by investigating the utility of over 40 features that have been used in recent literature. Information gain for these features are calculated over Ham, Spam and Phishing corpora.

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Section: Related Workmentioning

confidence: 99%

Feature selection for Spam and Phishing detection

Toolan

Carthy

2010

2010 eCrime Researchers Summit

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“…Another widely deployed technique used multi classifier related with machine learning for phishing email detection is (Saberi et al, 2007), the proposed method accuracy detected 94.4% of phishing emails. An another approach depend on three tier classification to detect phishing emails is (Islam et al, 2009), if the first two classifier can't classify well the final tier will have the final decision , the average accuracy of this approach reach up to 97%.…”

Section: Methodsmentioning

confidence: 99%

Evolving Fuzzy Neural Network for Phishing Emails Detection

Almomani

Wan

Altaher

et al. 2012

Journal of Computer Science

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Abstract:One of the broadly used internet attacks to deceive customers financially in banks and agencies is unknown "zero-day" phishing Emails "zero-day" phishing Emails is a new phishing email that it has not been trained on old dataset, not included in black list. Accordingly, the current paper seeks to Detection and Prediction of unknown "zero-day" phishing Emails by provide a new framework called Phishing Evolving Neural Fuzzy Framework (PENFF) that is based on adoptive Evolving Fuzzy Neural Network (EFuNN). PENFF does the process of detection of phishing email depending on the level of features similarity between body email and URL email features. The totality of the common features vector is controlled by EFuNN to create rules that help predict the phishing email value in online mode. The proposed framework has proved its ability to detect phishing emails by decreasing the error rate in classification process. The current approach is considered a highly compacted framework. As a performance indicator; the Root Mean Square Error (RMSE) and Non-Dimensional Error Index (NDEI) has 0.12 and 0.21 respectively, which has low error rate compared with other approaches Furthermore, this approach has learning capability with footprint consuming memory.

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“…There are other defensive mechanisms, such as preventing a phishing attack from reaching its intended recipient [13], [14]. However, it has been shown that there is a need to better protect victims from phishing attacks, as blacklists are not working [15].…”

Section: B Motivationmentioning

confidence: 99%

Phish-Net: Investigating phish clusters using drop email addresses

Zawoad

Dutta

Sprague

et al. 2013

2013 APWG eCrime Researchers Summit

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The most common approach to collect users' secret credentials from phishing websites is to email the credentials to criminals' email addresses which we call drop email addresses. We propose a clustering algorithm, which is based on the assumption that if there is a common drop email address found in the phishing kits from two different phishing websites, then these two websites are directly related. Based on obfuscated and plain-text drop email addresses, we produce two types of clusters: one is called phishing kit creator cluster and another is kit user cluster. Clustering related phishing websites using our proposed approach will allow phishing investigators to focus their investigative efforts on important phishing attacks rather than random attacks. For example, in January 2013, 1475 phishing websites are hosted by only 317 groups of phishers (who we will call kit users). Our scheme will thus help investigators to narrow investigation to pervasive phishing criminals. By analyzing the clusters generated using our clustering approach, we can determine the strongest and most pervasive phishers, and phishing kit creators, relationships between phishing kit creators and phishing kit users, and the most dominant phisher of one group. These findings have real-life implication in phishing investigation paradigm.

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Learn to Detect Phishing Scams Using Learning and Ensemble ?Methods

Cited by 9 publications

References 6 publications

Feature selection for Spam and Phishing detection

Feature selection for Spam and Phishing detection

Evolving Fuzzy Neural Network for Phishing Emails Detection

Phish-Net: Investigating phish clusters using drop email addresses

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