Phishing detection: A recent intelligent machine learning comparison based on models content and features

Abdelhamid, Neda; Thabtah, Fadi; Abdel-Jaber, Hussein

doi:10.1109/isi.2017.8004877

Cited by 71 publications

(39 citation statements)

References 16 publications

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“…Bottazzi et al [23] proposed a framework in Android mobile devices for phishing detection, which includes a machine learning detection engine for protecting from new phishing activities. Abdelhamid et al [14] investigated several machine-learning-based phishing detection techniques on their pros and cons, including evaluation with real-world dataset on their performance. In our preliminary work [24], we used two classifiers to detect phishing attacks from page layout features.…”

Section: Learning-based Phishing Detectionmentioning

confidence: 99%

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Phishing page detection via learning classifiers from page layout feature

Mao

Bian

Tian

et al. 2019

J Wireless Com Network

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The web technology has become the cornerstone of a wide range of platforms, such as mobile services and smart Internet-of-things (IoT) systems. In such platforms, users' data are aggregated to a cloud-based platform, where web applications are used as a key interface to access and configure user data. Securing the web interface requires solutions to deal with threats from both technical vulnerabilities and social factors. Phishing attacks are one of the most commonly exploited vectors in social engineering attacks. The attackers use web pages visually mimicking legitimate web sites, such as banking and government services, to collect users' sensitive information. Existing phishing defense mechanisms based on URLs or page contents are often evaded by attackers. Recent research has demonstrated that visual layout similarity can be used as a robust basis to detect phishing attacks. In particular, features extracted from CSS layout files can be used to measure page similarity. However, it needs human expertise in specifying how to measure page similarity based on such features. In this paper, we aim to enable automated page-layout-based phishing detection techniques using machine learning techniques. We propose a learning-based aggregation analysis mechanism to decide page layout similarity, which is used to detect phishing pages. We prototype our solution and evaluate four popular machine learning classifiers on their accuracy and the factors affecting their results.

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Section: Learning-based Phishing Detectionmentioning

confidence: 99%

“…Machine learning has been widely used in many areas to create automated solutions. Researchers also use machine learning to detect phishing attacks based on various features [10][11][12][13][14]. The solutions show the potential of machine learning techniques to detect phishing attacks.…”

Section: Introductionmentioning

confidence: 99%

Phishing page detection via learning classifiers from page layout feature

Mao

Bian

Tian

et al. 2019

J Wireless Com Network

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“…41 Ten-fold cross-validation was used to train the classifiers. 42,43 The choice of these algorithms was based on their common use in practical applications and the different learning methods employed during the training process. 44,[45][46] .…”

Section: Specificity =mentioning

confidence: 99%

Ensemble Learning Model for Screening Autism in Children

Diabat¹,

Al-Shanableh²

2019

IJCSIT

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Autistic Spectrum Disorder (ASD) is a neurological condition associated with communication, repetitive, and social challenges. ASD screening is the process of detecting potential autistic traits in individuals using tests conducted by a medical professional, a caregiver, or a parent. These tests often contain large numbers of items to be covered by the user and they generate a score based on scoring functions designed by psychologists and behavioural scientists. Potential technologies that may improve the reliability and accuracy of ASD tests are Artificial Intelligence and Machine Learning. This paper presents a new framework for ASD screening based on Ensembles Learning called Ensemble Classification for Autism Screening (ECAS). ECAS employs a powerful learning method that considers constructing multiple classifiers from historical cases and controls and then utilizes these classifiers to predict autistic traits in test instances. ECAS performance has been measured on a real dataset related to cases and controls of children and using different Machine Learning techniques. The results revealed that ECAS was able to generate better classifiers from the children dataset than the other Machine Learning methods considered in regard to levels of sensitivity, specificity, and accuracy.

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“…Phishing can be de¯ned according to (Abdelhamid et al, 2017) as imitating a legitimate website by designing a visually similar fake website to defraud online users to unlawfully obtain their credentials to access their¯nancial assets. Phishing can be seen as a criminal act and a serious web threat because of the substantial¯nancial damages it can cause (Mohammad et al, 2015a).…”

Section: Introductionmentioning

confidence: 99%

Visualisation Model Based on Phishing Features

Rajab

2019

J. Info. Know. Mgmt.

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The numbers of online purchases and electronic banking transactions have increased substantially in the era of electronic business and mobile commerce. These online financial activities have attracted a special web threat called “phishing” that targets Internet users by seeking their credentials in order to access their financial information. Phishing involves impersonating a legitimate website by creating a visually similar fake website to deceive users. In the last decade different solutions to fight phishing that are primarily based on educating users, user’s experience, search methods, machine learning and features similarity have been developed. This paper combines computational intelligence along with user’s experience approaches to develop an anti-phishing visualisation method. Our method employs effective features chosen following thorough analysis on features scores generated by Correlation Feature Set and Information Gain processing techniques. We validate our anti-phishing features using classification systems produced by rule induction data mining approach. False positives, false negatives and phishing detection rate are the basis of evaluating the classification systems to measure our anti-phishing methods features’ integrity.

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Phishing detection: A recent intelligent machine learning comparison based on models content and features

Cited by 71 publications

References 16 publications

Phishing page detection via learning classifiers from page layout feature

Phishing page detection via learning classifiers from page layout feature

Ensemble Learning Model for Screening Autism in Children

Visualisation Model Based on Phishing Features

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