Phishing Emails Detection Using CS-SVM

Niu, Weina; Zhang, Xiaosong; Yang, Guowu; Ma, Zhiyuan; Zhuo, Zhongliu

doi:10.1109/ispa/iucc.2017.00160

Cited by 29 publications

(18 citation statements)

References 4 publications

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“…This method uses the measures of a true positive rate, a false positive rate, and an accuracy as evaluation metric to evaluate the performance. CS-SVM shows a 91 percent higher result in terms of phishing email detection accuracy at different training sets when compared with traditional SVM classifier [10].…”

Section: Related Workmentioning

confidence: 96%

See 1 more Smart Citation

Fuzzy Modelling using Firefly Algorithm for Phishing Detection

Syahirah¹,

Arfian²,

Mezhuyev³

et al. 2019

Adv. sci. technol. eng. syst. j.

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A fuzzy system is a rule-based system that uses human experts' knowledge to make a particular decision, while fuzzy modeling refers to the identification process of the fuzzy parameters. To generate the fuzzy parameters automatically, an optimization method is needed. One of the suitable methods provides the Firefly Algorithm (FA). FA is a natureinspired algorithm that uses fireflies' behavior to interpret data. This study explains in detail how fuzzy modeling works by using FA for detecting phishing. Phishing is an unsettled security problem that occurs in the world of internet connected computers. In order to experiment with the proposed method for the security threats, a database of phishing websites and SMS from different sources were used. As a result, the average accuracy for the phishing websites dataset achieved 98.86%, while the average value for the SMS dataset is 97.49%. In conclusion, both datasets show the best result in terms of the accuracy value for fuzzy modeling by using FA.

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Section: Related Workmentioning

confidence: 96%

“…The method [9] is not flexible to accommodate the increasing number of consumers, therefore it will be difficult to provide a unique code to each user. Authors [10] proposed phishing emails detection using Cuckoo Search SVM (CS-SVM). Cuckoo Search algorithm was used for parameter selection.…”

Section: Related Workmentioning

confidence: 99%

Fuzzy Modelling using Firefly Algorithm for Phishing Detection

Syahirah¹,

Arfian²,

Mezhuyev³

et al. 2019

Adv. sci. technol. eng. syst. j.

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show abstract

“…Over the years, cyber-security firms have extensively utilized Machine Learning for detecting Phishing URLs and malicious sites. These works ranged from SVM [52,20,28], Random Forest [40,19], KNN [41,45] and a combinations of this techniques [8,32]. After the advent of Deep Learning, Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) have become the norm for automated feature extraction from a huge volume of data without any human intervention.…”

Section: Literature Reviewmentioning

confidence: 99%

Semi-supervised Conditional GAN for Simultaneous Generation and Detection of Phishing URLs: A Game theoretic Perspective

Kamran¹,

Sengupta²,

Tavakkoli³

2021

Preprint

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Spear Phishing is a type of cyber-attack where the attacker sends hyperlinks through email on well-researched targets. The objective is to obtain sensitive information such as name, credentials, credit card numbers, or other crucial data by imitating oneself as a trustworthy website. According to a recent report, phishing incidents nearly doubled in frequency in 2020. In recent times, machine learning techniques have become the standard for defending against such attacks. Many augmentations have been made for improving the existing architectures, such as Convolutional Networks, Recurrent Networks, and Generative Adversarial Networks. However, these architectures were designed with only defense in mind. Moreover, the attacker's perspective and motivation are absent while creating such training and deployment pipelines. To address this, we need a gametheoretic approach to understand the rational decision-making process of the attacker (Hacker) and the defender (Phishing URL detector). We propose a Conditional Generative Adversarial Network for real-time phishing URL detection. Additionally, we train our architecture in a semi-supervised manner to distinguish between adversarial and real examples, along with detecting malicious and benign URLs. We also design two games between the attacker and defender in training and deployment settings by utilizing the game-theoretic perspective. Our experiments confirm that the proposed architecture surpasses recent state-of-theart architectures for phishing URLs detection.

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“…An email is divided into three components: Email header, Email body and the URL for more detailed extraction. The second stage focuses on phishing email classifier using Cuckoo Search (CS) and Support Vector Machine (SVM) algorithm to produce the result the verify whether the email is phishing or non-phishing email [4].…”

Section: Related Workmentioning

confidence: 99%

Web Based Phishing Email Identifier Using Sender Address and Returning Address

2020

jcr

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Phishing emails are the emails that contains attachment(s) such as URL(s) or any file attachment(s) and messages that convinces user to access the URL or the attachment file whereby the URL redirects user to a fake website and prompt user for inputs of confidential while file attachment might contain any malicious software that can infected the user's device. The target victims of these attacks are ranging from regular webmail users to professionals and the victims are usually targeted randomly as well as other well-planned attacks. Therefore, general webmail users should know the basic of phishing email identification based on the existing information included in inbox mails. In this paper, researcher analyzed different algorithms proposed and how will the algorithms contribute to phishing emails detection. Based on the result, researcher realized the importance for general webmail users to have the knowledge in identifying phishing emails. The Web Based Phishing Email Identifier is developed to detect phishing emails in basic level as well as educating the users on characteristics of phishing emails.

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Phishing Emails Detection Using CS-SVM

Cited by 29 publications

References 4 publications

Fuzzy Modelling using Firefly Algorithm for Phishing Detection

Fuzzy Modelling using Firefly Algorithm for Phishing Detection

Semi-supervised Conditional GAN for Simultaneous Generation and Detection of Phishing URLs: A Game theoretic Perspective

Web Based Phishing Email Identifier Using Sender Address and Returning Address

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