An intelligent classification model for phishing email detection

Yasin, Adwan; Abuhasan, Abdelmunem

doi:10.48550/arxiv.1608.02196

Cited by 3 publications

(7 citation statements)

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“…The decision tree can be applied to the categorical or continuous variables. Instances of research in the literature using DT are [48]- [50], [56]- [62], [64], [67], [75], [87]- [101].…”

Section: ) Decision Tree (Dt)mentioning

confidence: 99%

“…Quick convergence and simplicity are the classifiers benefits, yet it is not possible to understand the associations and interactions amongst the features of each of the samples. The following papers [44], [50], [53]- [56], [58], [61], [63], [64], [66], [71], [73], [77], [80], [85], [87], [89], [91], [94], [97], [101], [102], [105]- [114] have reported the use of NB to enhance the textual features in phishing email detection.…”

Section: ) Naïve Bayes (Nb)mentioning

confidence: 99%

“…Yet, it is difficult to interpret the SVM, and it is quite memory sensitive. We noticed that numerous scientific papers, such as [44]- [50], [53]- [56], [58], [61], [63], [66], [70], [71], [73], [80], [87]- [89], [91], [94], [96], [97], [99], [101], [102], [106]- [110], [112], [114]- [128] have used SVM algorithm to detect phishing emails.…”

Section: ) Support Vector Machine (Svm)mentioning

confidence: 99%

“…The local minima are quite standard, and there is need for delicate regularisation. Many papers [53], [54], [66], [70], [87], [91], [94], [102], [105], [111], [112], [131]- [133] have used NN differently.…”

Section: ) Neural Network (Nn)mentioning

confidence: 99%

“…There are some more metrics recorded in certain papers , for instance, area under curve (AUC), and receiver operating characteristics (ROC) [51], [55], [59], [61], [87], [92], [98], [132], [153], [164]. Moreover, when taking into account imbalanced datasets, the utilisation of relevant evaluation metrics is an essential aspect with which to reckon.…”

Section: Actual Positive Tp Fn Actual Negative Fp Tnmentioning

confidence: 99%

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A Systematic Literature Review on Phishing Email Detection Using Natural Language Processing Techniques

et al. 2022

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Every year, phishing results in losses of billions of dollars and is a major threat to the Internet economy. Phishing attacks are now most often carried out by email. To better comprehend the existing research trend of phishing email detection, several review studies have been performed. However, it is important to assess this is sue from different perspectives . None of the surveys have ever comprehensively studied the use of Natural Language Processing (NLP) techniques for detection of phishing except one that shed light on the use of NLP techniques for classification and training purposes , while exploring a few alternatives. To bridge the gap, this study aims to systematically review and synthesise research on the use of NLP for detecting phishing emails. Based on specific predefined criteria, a total of 100 research articles published between 2006 and 2022 were identified and analysed. We study the key research areas in phishing email detection using NLP, machine learning algorithms used in phishing detection email, text features in phishing emails, datasets and resources that have been used in phishing emails, and the evaluation criteria. The findings include that the main research area in phishing detection studies is feature extraction and selection, followed by methods for classifying and optimizing the detection of phishing emails. Amongst the range of classification algorithms, support vector machines (SVMs) are heavily utilised for detecting phishing emails . The most frequently used NLP techniques are found to be TF-IDF and word embeddings. Furthermore, the most commonly used datasets for benchmarking phishing email detection methods is the Nazario phishing corpus. Also, Python is the most commonly used one for phishing email detection. It is expected that the findings of this paper can be helpful for the scientific community, especially in the field of NLP application in cybersecurity problems. This survey also is unique in the sense that it relates works to their openly available tools and resources. The analysis of the presented works revealed that not much work had been performed on Arabic language phishing emails using NLP techniques. Therefore, many open issues are associated with Arabic phishing email detection.INDEX TERMS Phishing email detection; systematic literature review; natural language processing; machine learning.

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“…The decision tree can be applied to the categorical or continuous variables. Instances of research in the literature using DT are [48]- [50], [56]- [62], [64], [67], [75], [87]- [101].…”

Section: ) Decision Tree (Dt)mentioning

confidence: 99%

Section: ) Naïve Bayes (Nb)mentioning

confidence: 99%

Section: ) Support Vector Machine (Svm)mentioning

confidence: 99%

Section: ) Neural Network (Nn)mentioning

confidence: 99%

Section: Actual Positive Tp Fn Actual Negative Fp Tnmentioning

confidence: 99%

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A Systematic Literature Review on Phishing Email Detection Using Natural Language Processing Techniques

et al. 2022

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Email phishing: Text classification using natural language processing

Verma,

Goyal,

Gigras

2020

Comput Sci Inf Technol

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Phishing is networked theft in which the main motive of phishers is to steal any person’s private information, its financial details like account number, credit card details, login information, payment mode information by creating and developing a fake page or a fake web site, which look completely authentic and genuine. Nowadays email phishing has become a big threat to all, and is increasing day by day. Moreover, detection of phishing emails has been considered an important research issue as phishing emails have been increasing day by day. Various techniques have been introduced and applied to deal with such a big issue. The major objective of this research paper is giving a detailed description on the classification of phishing emails using the natural language processing concepts. NLP (natural language processing) concepts have been applied for the classification of emails, along with that accuracy rate of various classifiers have been calculated. The paper is presented in four sections. An introduction about phishing its types, its history, statistics, life cycle, motivation for phishers and working of email phishing have been discussed in the first section. The second section covers various technologies of phishing- email phishing and also description of evaluation metrics. An overview of the various proposed solutions and work done by researchers in this field in form of literature review has been presented in the third section. The solution approach and the obtained results have been defined in the fourth section giving a detailed description about NLP concepts and working procedure.

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Cybersecurity Awareness: Investigating Students’ Susceptibility to Phishing Attacks for Sustainable Safe Email Usage in Academic Environment (A Case Study of a Nigerian Leading University)

Okokpujie¹,

Kennedy²,

Nnodu³

et al. 2023

IJSDP

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With the advancement in information communication technology (ICT), cyber-attacks have become a global phenomenon, with email phishing at the topmost. Academic institutions' ICT infrastructures are one of many targets, thus the need to facilitate cybersecurity awareness among students. This research is aimed at investigating students’ susceptibility to phishing attacks for sustainable safe electronic mail (email) usage in the academic environment. Two email phishing tests were carried out during this research work to discover how students reacted to phish emails and understand how students respond to phish emails where all group members are recipients. Finally, questionnaires are administered to participants after completing the exercise to ascertain the students' awareness of phishing attacks based on received emails. The results show that 70.6% of college students surveyed are susceptible to this form of attack due to unawareness. In conclusion, recommendations are outlined on securing the academic community and ICT infrastructures to achieve a sustainable and Safe email usage environment.

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An intelligent classification model for phishing email detection

Cited by 3 publications

References 0 publications

A Systematic Literature Review on Phishing Email Detection Using Natural Language Processing Techniques

A Systematic Literature Review on Phishing Email Detection Using Natural Language Processing Techniques

Email phishing: Text classification using natural language processing

Cybersecurity Awareness: Investigating Students’ Susceptibility to Phishing Attacks for Sustainable Safe Email Usage in Academic Environment (A Case Study of a Nigerian Leading University)

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