A domain-feature enhanced classification model for the detection of Chinese phishing e-Business websites

Zhang, Dongsong; Yan, Zhijun; Jiang, Hansi; Kim, Taeha

doi:10.1016/j.im.2014.08.003

Cited by 61 publications

(39 citation statements)

References 14 publications

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“…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.

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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.

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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.

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“…In CANTINA [8], it is a content-based approach to detect phishing, besides the URL and its domain name basically, CANTINA use TF-IDF algorithm to retrieve information, TF-IDF can measure how important a word in a document, and from their experiments result, it is good at detecting phishing, and using TF-IDF can reduce FP rate effectively. In [9], they proposed a method for e-Business websites phishing detection, it combined several features of business websites in Chinese, and use four classification algorithms to decide whether a phishing site. Besides, they consider that researchers should develop domain-specific methods which can detect phishing more useful.…”

Section: Related Workmentioning

confidence: 99%

The Novel Features for Phishing Based on User Device Detection

Lin¹,

Chi²,

Chuang³

et al. 2016

JCP

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Recent years the rapid developments of technology, Internet services are gradually depending on the environment to be able to provide different services. Due to the rise of mobile devices, in order to provide the most appropriate service to the users, in addition to desktop websites, the most popular sites are beginning to build a websites for mobile devices exclusive service users at the same time. However, phishing website, the phishers will not necessarily build two kinds of websites at the same time.In this paper, we propose the new phishing features through detect device according the situation. In the experiment, we through the transfer user agent of desktop and mobile to connect, and use SVM to classify, from the result, we find there are the mechanisms in most popular websites, but in phishing websites, the number of this mechanism is rarely.

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“…Nevertheless, they varied in selecting the most contributing features such that classifiers caused variation on detection accuracies. Later, Zhang, Jiang, and Kim [27] developed automatic detection approach for Chinese e-business websites by incorporating the unique features extracted from URL and contents of website. Alongside, Hamid and Abawajy [28] proposed a multi-tier detector to phish emails filtering with the aid of Adaboost and SMO classifiers in an ensemble design.…”

Section: B Related Workmentioning

confidence: 99%

“…Such limitations include: the dependency of feature selection outcomes on a given dataset, different feature selection outcomes across different classification models, heterogeneity of features values, and un-scalable feature selection method to more challenging datasets [23][24][25][26][27][28]. Furthermore, most of the dedicated efforts focused on discarding the relevant features rather than the redundant ones during feature selection [23][24][25][26][27][28]. Besides, since they are mutually dependent on other features belonging to the same targeting class; the redundant features might distort the classification task and then degrade its accuracy by producing high error rates [29 and 30].…”

Section: Shortagesmentioning

confidence: 99%

See 1 more Smart Citation

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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A domain-feature enhanced classification model for the detection of Chinese phishing e-Business websites

Cited by 61 publications

References 14 publications

Application of Feature Engineering for Phishing Detection

Application of Feature Engineering for Phishing Detection

The Novel Features for Phishing Based on User Device Detection

The Effect of Feature Selection on Phish Website Detection

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