Phishing Websites Classification using Association Classification (PWCAC)

Alqahtani, Mohammed A.

doi:10.1109/iccisci.2019.8716444

Cited by 12 publications

(14 citation statements)

References 22 publications

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“…class attribute with two (2) labels), the confusion matrix is used and values for each performance metric were calculated using the results obtained from each model confusion matrix. The performances of the developed proposed models were evaluated using the following performances as widely used to evaluate existing methods for phishing website detection [3]. These metrics include Accuracy, False Positive (FP), False Negative (FN) and F-measure.…”

Section: Performance Evaluation Methodsmentioning

confidence: 99%

“…AdaBoost-Extra Tree (ABET), Bagging -Extra tree (BET), Rotation Forest -Extra Tree (RoFBET) and LogitBoost-Extra Tree (LBET) phishing website detection methods. 3. Comparative analysis of recent ML phishing website detection methods against the proposed methods of this study.…”

Section: Proposal Development and Implementation Ofmentioning

confidence: 99%

“…Phishing is carried out via email and or websites that contain malicious content for stealing information from an uninformed or inattentive user of the Internet. As a result of the fast-growing and evolving nature of phishing, existing countermeasures are being developed in three different approaches vis-à-vis education, legal and technical approaches [3]. This study is based on a technical (i.e.…”

Section: Related Workmentioning

confidence: 99%

“…More so, the chance for improving the method performance exist. The research conducted by Alqahtani [3] produced a novel method for detecting phishing websites. This method was referred to as Phishing Websites Classification using Association Classification (PWCAC).…”

Section: Related Workmentioning

confidence: 99%

“…Hence, phishing disastrous outcomes emphasized the imperative need for developing effective and efficient solutions or methods to curb it [2]. It is a known fact that the occurrence of phishing attacks is no longer limited to SMS, pop-ups, and e-mails but also spreads to QR codes and spoof mobile applications [3]. A number of latest phishing techniques are hosted or linked to websites [4].…”

Section: Introductionmentioning

confidence: 99%

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AI Meta-Learners and Extra-Trees Algorithm for the Detection of Phishing Websites

et al. 2020

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Phishing is a type of social web-engineering attack in cyberspace where criminals steal valuable data or information from insensitive or uninformed users of the internet. Existing countermeasures in the form of anti-phishing software and computational methods for detecting phishing activities have proven to be effective. However, new methods are deployed by hackers to thwart these countermeasures. Due to the evolving nature of phishing attacks, the need for novel and efficient countermeasures becomes crucial as the effect of phishing attacks are often fatal and disastrous. Artificial Intelligence (AI) schemes have been the cornerstone of modern countermeasures used for mitigating phishing attacks. AI-based phishing countermeasures or methods possess their shortcomings particularly the high false alarm rate and the inability to interpret how most phishing methods perform their function. This study proposed four (4) meta-learner models (AdaBoost-Extra Tree (ABET), Bagging -Extra tree (BET), Rotation Forest -Extra Tree (RoFBET) and LogitBoost-Extra Tree (LBET)) developed using the extra-tree base classifier. The proposed AI-based meta-learners were fitted on phishing website datasets (currently with the newest features) and their performances were evaluated. The models achieved a detection accuracy not lower than 97% with a drastically low false-positive rate of not more 0.028. In addition, the proposed models outperform existing MLbased models in phishing attack detection. Hence, we recommend the adoption of meta-learners when building phishing attack detection models.

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Section: Performance Evaluation Methodsmentioning

confidence: 99%

Section: Proposal Development and Implementation Ofmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%