Heuristic nonlinear regression strategy for detecting phishing websites

Babagoli, Mehdi; Aghababa, Mohammad Pourmahmood; Solouk, Vahid

doi:10.1007/s00500-018-3084-2

Cited by 92 publications

(54 citation statements)

References 30 publications

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“…The first-ranked feature has highest correlation with the target class and lowest correlation with the others [13]. DW utilizes a combination of decision tree and Wrapper to select an optimal feature set [14]. Since IG and CFS just provide a ranking of features, a threshold is needed to specify which features should be selected as efficient.…”

Section: Methodsmentioning

confidence: 99%

“…Furthermore, DW is dependent on the expert opinion to decide what decreasing in the accuracy should be considered as significant to stop the method. In this work, the thresholds and other settings are set based on the values suggested in [13] and [14]. Figure 2 presents the F-measure values for classifiers using four feature selection algorithms over each data set.…”

Section: Methodsmentioning

confidence: 99%

“…For example, it is feasible that the set of features best distinguishing phishing websites that imitate a financial institution may be different from sites imitating an e-commerce platform, or of websites that are encoded in different languages. The present art in feature selection are simply based on heuristic thresholds [13], [14].…”

Section: Introductionmentioning

confidence: 99%

“…This training set which is composed by random extraction of websites from an online repository [16] contains 7,000 legitimate and 7,000 phishing website samples. The benefit of using FRS for feature selection is realized by comparing our results against three recently proposed algorithms [13], [14].…”

Section: Introductionmentioning

confidence: 99%

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Fuzzy Rough Set Feature Selection to Enhance Phishing Attack Detection

Zabihimayvan

Doran

2019

2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

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Phishing as one of the most well-known cybercrime activities is a deception of online users to steal their personal or confidential information by impersonating a legitimate website. Several machine learning-based strategies have been proposed to detect phishing websites. These techniques are dependent on the features extracted from the website samples. However, few studies have actually considered efficient feature selection for detecting phishing attacks. In this work, we investigate an agreement on the definitive features which should be used in phishing detection. We apply Fuzzy Rough Set (FRS) theory as a tool to select most effective features from three benchmarked data sets. The selected features are fed into three often used classifiers for phishing detection. To evaluate the FRS feature selection in developing a generalizable phishing detection, the classifiers are trained by a separate out-of-sample data set of 14,000 website samples. The maximum F-measure gained by FRS feature selection is 95% using Random Forest classification. Also, there are 9 universal features selected by FRS over all the three data sets. The F-measure value using this universal feature set is approximately 93% which is a comparable result in contrast to the FRS performance. Since the universal feature set contains no features from third-part services, this finding implies that with no inquiry from external sources, we can gain a faster phishing detection which is also robust toward zero-day attacks.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Fuzzy Rough Set Feature Selection to Enhance Phishing Attack Detection

Zabihimayvan

Doran

2019

2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

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“…Choice of feature is an important task to build a good, generalized phishing detection. Currently, a feature that is widely used as an option is heuristics (Babagoli, Aghababa, & Solouk, 2019).…”

Section: Literature Reviewmentioning

confidence: 99%

A Survey on Phishing Website Detection Using Hadoop

Natadimadja

Abdurohman

Nuha

2020

JIUP

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Phishing is an activity carried out by phishers with the aim of stealing personal data of internet users such as user IDs, password, and banking account, that data will be used for their personal interests. Average internet user will be easily trapped by phishers due to the similarity of the websites they visit to the original websites. Because there are several attributes that must be considered, most of internet user finds it difficult to distinguish between an authentic website or not. There are many ways to detecting a phishing website, but the existing phishing website detection system is too time-consuming and very dependent on the database it has. In this research, the focus of Hadoop MapReduce is to quickly retrieve some of the attributes of a phishing website that has an important role in identifying a phishing website, and then informing to users whether the website is a phishing website or not.

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Intelligent antiphishing framework to detect phishing scam: A hybrid classification approach

Marimuthu¹,

Gopalasamy²,

Ben‐Othman

2021

Softw Pract Exp

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In the inevitably hi‐tech universe of cybercrimes, one of the major still prevailing methods is the usage of the malicious URLs and creating a Phishing link to obtain user credentials from the people. This method is highly subtle and has more effect on people's lives as well as corporate loss. To identify the malicious URLs, the security community has listed blacklists and benign links online. Still, as the technology is being developed day by day, the attackers try to create new phishing URLs using new social engineering methods that could be easily forged into the user's account. To improve the generality of malicious URL detectors, machine‐learning techniques have been explored with increasing attention in recent years. This article addresses the detection of malicious URLs combining the intelligence of both the heuristic‐based method and the machine learning process. It has been found that there are many possibilities for detecting zero‐day attacks and spear‐phishing attacks when incorporating both lexical features and machine learning methods. Out of the six‐batch learning process analyzed, we implement a decision tree algorithm in our framework with 99.47% accuracy during evaluation. The true positive values gained in our proposed hybrid framework is 99.2% and indicate <1% of the false‐positive values. The implementation shows a precision level higher than the previous model developed and by other antiphishing techniques. A high detection rate on zero‐day and spear‐phishing attacks and overall results reveal that our system outclasses the current approach to detecting phishing scams.

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Heuristic nonlinear regression strategy for detecting phishing websites

Cited by 92 publications

References 30 publications

Fuzzy Rough Set Feature Selection to Enhance Phishing Attack Detection

Fuzzy Rough Set Feature Selection to Enhance Phishing Attack Detection

A Survey on Phishing Website Detection Using Hadoop

Intelligent antiphishing framework to detect phishing scam: A hybrid classification approach

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