Assessing the severity of phishing attacks: A hybrid data mining approach

Chen, Xi; Bose, Indranil; Leung, Alvin Chung Man; Guo, Chenhui

doi:10.1016/j.dss.2010.08.020

Cited by 67 publications

(28 citation statements)

References 25 publications

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“…Chen et al [12] have proposed a hybrid approach that mixes extraction of key phrase, textual, financial data to ascertain the vicious of phishing attack using supervised classification strategies. Nishanth et al [13] have proposed a method in which the structured style of the financial data are mined using machine learning algorithms.…”

Section: Related Workmentioning

confidence: 99%

Intelligent phishing url detection using association rule mining

Jeeva

Rajsingh

2016

Hum. Cent. Comput. Inf. Sci.

108

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Phishing is an online criminal act that occurs when a malicious webpage impersonates as legitimate webpage so as to acquire sensitive information from the user. Phishing attack continues to pose a serious risk for web users and annoying threat within the field of electronic commerce. This paper focuses on discerning the significant features that discriminate between legitimate and phishing URLs. These features are then subjected to associative rule mining—apriori and predictive apriori. The rules obtained are interpreted to emphasize the features that are more prevalent in phishing URLs. Analyzing the knowledge accessible on phishing URL and considering confidence as an indicator, the features like transport layer security, unavailability of the top level domain in the URL and keyword within the path portion of the URL were found to be sensible indicators for phishing URL. In addition to this number of slashes in the URL, dot in the host portion of the URL and length of the URL are also the key factors for phishing URL.

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Section: Related Workmentioning

confidence: 99%

Intelligent phishing url detection using association rule mining

Jeeva

Rajsingh

2016

Hum. Cent. Comput. Inf. Sci.

108

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“…A new phishing webpage detection approach proposed by Zhao et al [3] based on a kind of semi-supervised learning method-transductive support vector machine (TSVM). The features of web image are extracted for complementing the disadvantage of phishing detection only based on document object model (DOM); includes gray histogram, color histogram, and spatial relationship between sub graphs.…”

Section: A Semi-supervised Learning Approach For Detection Of Phishinmentioning

confidence: 99%

“…In contrast to the drawback of support vector machine (SVM) algorithm which simply trains classifier by learning little and poor representative labeled samples, this method introduces the TSVM to train classifier that it takes into account the distribution information implicitly embodied in the large quantity of the unlabeled samples, and have better performance than SVM. Zhao et al [3] extracted the features of web page image in addition to that the disadvantage of detection based on DOM objects and properties are complemented. The features include web image, DOM objects that reflect the characteristics of web pages absolutely.…”

Section: A Semi-supervised Learning Approach For Detection Of Phishinmentioning

confidence: 99%

Improving Phishing URL Detection Using Fuzzy Association Mining

Nivedha¹,

Gokulan²,

Karthik³

et al. 2017

THE IJES

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“…Using either MLP, PNN or DT, the achieved accuracy was above 80 %. Chen et al [16] classified not only the risk levels of phishing attacks but also its impact to market value of the attacked firms, by using TM and data mining in phishing alerts and firms' financial data and they also distinguished variables with significant impact in the seriousness of the attacks. Finally, Wang et al [17] conducted both quantitative and qualitative analysis in order to measure the financial impact of the information security incidents reported in firm financial reports.…”

Section: Related Workmentioning

confidence: 99%

Associating the Severity of Vulnerabilities with their Description

Toloudis

Spanos

Angelis

2016

Lecture Notes in Business Information Processing

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Abstract. Software vulnerabilities constitute a major problem for today's world, which relies more than ever to technological achievements. The characterization of vulnerabilities' severity is an issue of major importance in order to address them and extensively study their impact on information systems. That is why scoring systems have been developed for the ranking of vulnerabilities' severity. However, the severity scores are based on technical information and are calculated by combining experts' assessments. The motivation for the study conducted in this paper was the question of whether the severity of vulnerabilities is directly related to their description. Hence, the associations of severity scores and individual characteristics with vulnerability descriptions' terms were studied using Text Mining, Principal Components and correlation analysis techniques, applied to all vulnerabilities registered in the National Vulnerability Database. The results are promising for the determination of severity by the use of the description since significant correlations were found.

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Assessing the severity of phishing attacks: A hybrid data mining approach

Cited by 67 publications

References 25 publications

Intelligent phishing url detection using association rule mining

Intelligent phishing url detection using association rule mining

Improving Phishing URL Detection Using Fuzzy Association Mining

Associating the Severity of Vulnerabilities with their Description

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