An Intelligent Classification Model for Phishing Email Detection

Yasin, Adwan; Abuhasan, Abdelmunem

doi:10.5121/ijnsa.2016.8405

Cited by 55 publications

(46 citation statements)

References 30 publications

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“…In the retail industry, salespeople can build predictive models through data mining to understand who are most likely to respond to correspondence, thereby increasing sales. When enterprises apply data mining technologies, they should fully understand the advantages and disadvantages of various technologies and methods and select appropriate technologies for specific environments and tasks [16].…”

Section: Discussionmentioning

confidence: 99%

A Framework for Implementing Machine Learning algorithms using Data sets

Chandra¹,

Ranjith²,

Shanthisri³

et al. 2019

IJITEE

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The rapid development of cloud computing, big data, machine learning and datamining made information technology and human society to enter new era of technology. Statistical and mathematical analysis on data given a new way of research on prediction and estimation using samples and data sets. Data mining is a mechanism that explores and analyzes many dis-organized or dis-ordered data to obtain potentially useful information and model it based on different algorithms. Machine learning is an iterative process rather than a linear process that requires each step to be revisited as more is learned about the problem. We discussed different machine learning algorithms that can manipulate data and analyses datasets based on best cases for accurate results. Design and Implementation of a framework that is associated with different machine learning algorithms. This paper expounds the definition, model, development stage, classification and commercial application of machine learning, and emphasizes the role of machine learning in data mining by deploying the framework. Therefore, this paper summarizes and analyzes machine learning technology, and discusses the use of machine learning algorithms in data mining. Finally, the mathematical analysis along with results and graphical analysis is given

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Section: Discussionmentioning

confidence: 99%

A Framework for Implementing Machine Learning algorithms using Data sets

Chandra¹,

Ranjith²,

Shanthisri³

et al. 2019

IJITEE

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“…The size of most email features is relatively small, except for features that rely on the vocabulary of the whole dataset like TFIDF [27]. Weighing the features with algorithms like Information Gain (IG) makes it possible to get subsets of features that give better classification results [165]- [171]. Figure 13 shows the classification and clustering methods used in the phishing email detection literature and the number of papers that employed them.…”

Section: B Email Feature Extraction and Analysismentioning

confidence: 99%

“…25 We refer to [90] where the authors built a dataset with emails from Wikileaks archives and Phishbowls from different universities. [167], [177], [179], [180] [162], [163], [165], [166], [171], [184] [161], [188]- [190], [192], [196], [197] [168]- [170], [173], [178], [186], [195] [176], [181]…”

Section: Dataset Properties 1) Dataset Sources and Availabilitymentioning

confidence: 99%

SoK: A Comprehensive Reexamination of Phishing Research From the Security Perspective

Das

Baki

Aassal

et al. 2020

IEEE Commun. Surv. Tutorials

109

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Phishing and spear phishing are typical examples of masquerade attacks since trust is built up through impersonation for the attack to succeed. Given the prevalence of these attacks, considerable research has been conducted on these problems along multiple dimensions. We reexamine the existing research on phishing and spear phishing from the perspective of the unique needs of the security domain, which we call security challenges: real-time detection, active attacker, dataset quality and baserate fallacy. We explain these challenges and then survey the existing phishing/spear phishing solutions in their light. This viewpoint consolidates the literature and illuminates several opportunities for improving existing solutions. We organize the existing literature based on detection techniques for different attack vectors (e.g., URLs, websites, emails) along with studies on user awareness. For detection techniques we examine properties of the dataset, feature extraction, detection algorithms used, and performance evaluation metrics. This work can help guide the development of more effective defenses for phishing, spear phishing and email masquerade attacks of the future, as well as provide a framework for a thorough evaluation and comparison.

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“…Phishing email detection. Classifiers are also used to determine phishing emails [18], [38]. In this case, the attack information is the content of the body of an email, such as links and words.…”

Section: Applicability To Other Attack Informationmentioning

confidence: 99%

Toward Collaborative Defense Across Organizations

Sasaki

Yoshioka

Matsumoto

2018

Journal of Information Processing

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New attack methods, such as new malware and exploits are released every day. Attack information is essential to improve defense mechanisms. However, we can identify barriers against attack information sharing. One barrier is that most targeted organizations do not want to disclose the attack and incident information because they fear negative public relations caused by disclosing incident information. Another barrier is that attack and incident information include confidential information. To address this problem, we propose a confidentiality-preserving collaborative defense architecture that analyzes incident information without disclosing confidential information of the attacked organizations. To avoid disclosure of confidential information, the key features of the proposed architecture are (1) exchange of trained classifiers, e.g., neural networks, that represent abstract information rather than raw attack/incident information and (2) classifier aggregation via ensemble learning to build an accurate classifier using the information of the collaborative organizations. We implement and evaluate an initial prototype of the proposed architecture. The results indicate that the malware classification accuracy improved from 90.4% to 92.2% by aggregating five organization classifiers. We conclude that the proposed architecture is feasible and demonstrates practical performance. We expect that the proposed architecture will facilitate an effective and collaborative response to current attack-defense situations.

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An Intelligent Classification Model for Phishing Email Detection

Cited by 55 publications

References 30 publications

A Framework for Implementing Machine Learning algorithms using Data sets

A Framework for Implementing Machine Learning algorithms using Data sets

SoK: A Comprehensive Reexamination of Phishing Research From the Security Perspective

Toward Collaborative Defense Across Organizations

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