A new fast associative classification algorithm for detecting phishing websites

Hadi, Wael; Aburub, Faisal; Alhawari, Samer

doi:10.1016/j.asoc.2016.08.005

Cited by 86 publications

(44 citation statements)

References 6 publications

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“…Finding the best frequent patterns, alongside the optimum, minimal confidence and support play a critical role in the process that evaluates rules for CBA and MCAR, while other points are also noticed through the literature, for instance, "Fast Associative Classification Algorithm (FACA)" was proposed in (Hadi et al, 2016). The authors managed to enhance the speed of model building, and sort the rules generated, alongside considering the confidence and support for the generated rules.…”

Section: Literature Reviewmentioning

confidence: 99%

Predicting Autism Spectrum Disorder using Machine Learning Technique

Alwidian¹,

Elhassan²,

Ghnemat³

2020

IJRTE

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Autism Spectrum Disorder (ASD) is a psychiatric disorder that puts constraints on the ability to use of cognitive, linguistic, communicative, and social skills. Recently, many data mining techniques employed to serve this domain by determining the main features of the condition and the correlation between them. In this article, we investigate the Association Classification (AC) technique as a data mining technique in predicting whether an individual has autism or not. Accordingly, seven well-known algorithms are selected to conduct analysis and evaluation of the performance of the AC technique in term of identifying correlations between the features to help decide early on whether an individual has autism; this is particularly significant for children. The evaluation for the behavior and the performance in the prediction tasks for the AC algorithms was conducted for the common metrics of including Precision, Accuracy F-Measure as well as Recall. Finally, a comparative performance analysis among the algorithms was used as final result for the study. The results show better performance for the WCBA algorithm in most test scenarios with accuracy of 97 % although, the majority of algorithms exhibited excellent accuracy when applied in this domain.

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Section: Literature Reviewmentioning

confidence: 99%

Predicting Autism Spectrum Disorder using Machine Learning Technique

Alwidian¹,

Elhassan²,

Ghnemat³

2020

IJRTE

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“…They classified antiphishing approaches into eight groups and highlighted advanced anti-phishing methods. Hadi et al [7] used the Fast-Associative Classification Algorithm (FACA) for classifying phishing URLs. FACA works by discovering all frequent rule item sets and building a model for classification.…”

Section: Related Workmentioning

confidence: 99%

Phishing Websites Detection using Machine Learning

Kulkarni¹,

Brown²

2019

IJACSA

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Tremendous resources are spent by organizations guarding against and recovering from cybersecurity attacks by online hackers who gain access to sensitive and valuable user data. Many cyber infiltrations are accomplished through phishing attacks where users are tricked into interacting with web pages that appear to be legitimate. In order to successfully fool a human user, these pages are designed to look like legitimate ones. Since humans are so susceptible to being tricked, automated methods of differentiating between phishing websites and their authentic counterparts are needed as an extra line of defense. The aim of this research is to develop these methods of defense utilizing various approaches to categorize websites. Specifically, we have developed a system that uses machine learning techniques to classify websites based on their URL. We used four classifiers: the decision tree, Naïve Bayesian classifier, support vector machine (SVM), and neural network. The classifiers were tested with a data set containing 1,353 real world URLs where each could be categorized as a legitimate site, suspicious site, or phishing site. The results of the experiments show that the classifiers were successful in distinguishing real websites from fake ones over 90% of the time.

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“…The traditional AC technique (Hadi, Aburub, & Alhawari, 2016) is derived as the combination of association rule mining and the resultant classification, which played an important role in the decision-making process in many previous applications. The association rules aim to discover a correlation or association between items, while association rules-based classification is conducted for label prediction.…”

Section: Association Classification (Ac)mentioning

confidence: 99%

Hybridizing association rules with adaptive weighted decision fusion for personal credit assessment

Zhang

Huang

Zhao

2019

Systems Science & Control Engineering

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Credit risk has been one of the major challenges emerged from the banking industry in modern financial markets. Served as a typical method, association classification (AC) has been widely used for personal credit risk assessment. It focuses on the relationship between an item and a class based on mined association rules, where three measures, i.e. support, confidence and weighted Chi-square, are generally used to generate association rules. However, most of the existing approaches neglect the discrimination power differences from between items, between measures and between rules. To deal with this problem, in this paper we present a novel approach characterized by hybridizing association rules with adaptive weighted decision fusion (HAR-AWDF) for personal credit assessment. Here in measures and rules are all worked as classifiers, and adaptive weightings are assigned to items via information entropies computed by posterior probabilities of individual items, also to measures and rules with their classification performance. In particular, a threshold scheme is proposed to determine whether a rule is effective, thereby the final decision is made via weighted voting in classification. The experimental results obtained conducting our approach on German Credit Data set show that among all items of the applied credit data, property, savings account and credit history are vital to evaluate personal credit state, in terms of classification accuracy. ARTICLE HISTORY

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A new fast associative classification algorithm for detecting phishing websites

Cited by 86 publications

References 6 publications

Predicting Autism Spectrum Disorder using Machine Learning Technique

Predicting Autism Spectrum Disorder using Machine Learning Technique

Phishing Websites Detection using Machine Learning

Hybridizing association rules with adaptive weighted decision fusion for personal credit assessment

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