An entice resistant automatic phishing detection

Kordestani, Hossain; Shajari, Mehdi

doi:10.1109/ikt.2013.6620052

Cited by 5 publications

(5 citation statements)

References 20 publications

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“…A web browser plug-in and a cloud anti-malware suite with its clients installed at the end users are two examples of such applications. For real-time applications, a blacklist approach has shown to be effective in other cybersecurity-related fields (Chen et al, 2014;Kordestani and Shajari, 2013) fields. For further research into phishing NSIFNs and the development of various tools to address networks and the web services they host, security researchers, vendors, and authorities may find the blacklist to be a useful resource.…”

Section: Applications Limitations and Future Workmentioning

confidence: 99%

Detection of Phishing Websites Hosted in Name Server Flux Networks Using Machine Learning

Nagunwa

2024

Journal of Computer Science

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Attackers are increasingly using Name Server IP Flux Networks (NSIFNs) to run the domain name services of their phishing websites in order to extend the duration of their phishing operations. These networks host a name server that manages the Domain Name System (DNS) records of the websites on a network of compromised machines with frequently changing IP addresses. As a result, blacklisting the machines has less impact on stopping the services, lengthening their lifespan and that of the websites they support. High detection delays and the use of fewer, lesser varied detection features limit the proposed solutions for identifying the websites hosted in these networks, making them more susceptible to detection evasions. This study suggests a novel set of highly diverse features based on DNS, network, and host behaviors for fast and highly accurate detection of phishing websites hosted in NSIFNs using a Machine Learning (ML) approach. Using a variety of traditional and deep learning ML algorithms, the prediction performance of our features was assessed in the context of binary and multi-class classification tasks. Our approach achieved optimal accuracy rates of 98.59% and 90.41% for the binary and multi-class classification tasks, respectively. Our approach is a crucial step toward monitoring NSIFN components to mitigate phishing attacks efficiently.

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Section: Applications Limitations and Future Workmentioning

confidence: 99%

Detection of Phishing Websites Hosted in Name Server Flux Networks Using Machine Learning

Nagunwa

2024

Journal of Computer Science

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“…Examples of such applications are a web browser plug-in and a cloud anti-malware suite with its clients installed at the end users. A blacklist approach has proved to be efficient for real-time applications in other cybersecurity related areas [70,71]. The blacklist can also be a useful resource for security researchers, vendors and authorities for further investigations on the operations of phishing FFSNs.…”

Section: Applications Limitations and Future Workmentioning

confidence: 99%

A machine learning approach for detecting fast flux phishing hostnames

Nagunwa

Kearney

Fouad

2022

Journal of Information Security and Applications

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“…Contrarily, a phish webpage detector was proposed by Li, Xiao, Feng, and Zhao [20] based on visual features and DOM objects of the webpage content that learned and tested over datasets by using Semi-Supervised Machine Learning (TSVM) classifier. Furthermore, Kordestani and Shajari [21] applied three classifiers, including Naïve Bayes (NB), Supervised Machine Learning (SVM), and Random Forest (RF), on a randomly selected dataset to predict phishness in suspected websites. They were deployed for phishness prediction with the presence of URL and online features.…”

Section: B Related Workmentioning

confidence: 99%

“…While, Alexa archive is publicly used to retrieve valid legitimate webpages. We chose such archives because they were commonly used by prior researchers in the literature of phishing detection [15][16][17][18][19][20][21][22][23][24][25][26][27][28]. Fig.…”

Section: A Test-bed and Featuresmentioning

confidence: 99%

The Effect of Feature Selection on Phish Website Detection

Zuhair¹,

Selmat²,

Salleh³

2015

ijacsa

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Abstract-Recently, limited anti-phishing campaigns have given phishers more possibilities to bypass through their advanced deceptions. Moreover, failure to devise appropriate classification techniques to effectively identify these deceptions has degraded the detection of phishing websites. Consequently, exploiting as new; few; predictive; and effective features as possible has emerged as a key challenge to keep the detection resilient. Thus, some prior works had been carried out to investigate and apply certain selected methods to develop their own classification techniques. However, no study had generally agreed on which feature selection method that could be employed as the best assistant to enhance the classification performance. Hence, this study empirically examined these methods and their effects on classification performance. Furthermore, it recommends some promoting criteria to assess their outcomes and offers contribution on the problem at hand. Hybrid features, low and high dimensional datasets, different feature selection methods, and classification models were examined in this study. As a result, the findings displayed notably improved detection precision with low latency, as well as noteworthy gains in robustness and prediction susceptibilities. Although selecting an ideal feature subset was a challenging task, the findings retrieved from this study had provided the most advantageous feature subset as possible for robust selection and effective classification in the phishing detection domain.

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An entice resistant automatic phishing detection

Cited by 5 publications

References 20 publications

Detection of Phishing Websites Hosted in Name Server Flux Networks Using Machine Learning

Detection of Phishing Websites Hosted in Name Server Flux Networks Using Machine Learning

A machine learning approach for detecting fast flux phishing hostnames

The Effect of Feature Selection on Phish Website Detection

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