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Erschienen in: Neural Computing and Applications 12/2017

17.03.2016 | Review

Fighting against phishing attacks: state of the art and future challenges

verfasst von: B. B. Gupta, Aakanksha Tewari, Ankit Kumar Jain, Dharma P. Agrawal

Erschienen in: Neural Computing and Applications | Ausgabe 12/2017

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Abstract

In the last few years, phishing scams have rapidly grown posing huge threat to global Internet security. Today, phishing attack is one of the most common and serious threats over Internet where cyber attackers try to steal user’s personal or financial credentials by using either malwares or social engineering. Detection of phishing attacks with high accuracy has always been an issue of great interest. Recent developments in phishing detection techniques have led to various new techniques, specially designed for phishing detection where accuracy is extremely important. Phishing problem is widely present as there are several ways to carry out such an attack, which implies that one solution is not adequate to address it. Two main issues are addressed in our paper. First, we discuss in detail phishing attacks, history of phishing attacks and motivation of attacker behind performing this attack. In addition, we also provide taxonomy of various types of phishing attacks. Second, we provide taxonomy of various solutions proposed in the literature to detect and defend from phishing attacks. In addition, we also discuss various issues and challenges faced in dealing with phishing attacks and spear phishing and how phishing is now targeting the emerging domain of IoT. We discuss various tools and datasets that are used by the researchers for the evaluation of their approaches. This provides better understanding of the problem, current solution space and future research scope to efficiently deal with such attacks.

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Metadaten
Titel
Fighting against phishing attacks: state of the art and future challenges
verfasst von
B. B. Gupta
Aakanksha Tewari
Ankit Kumar Jain
Dharma P. Agrawal
Publikationsdatum
17.03.2016
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 12/2017
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-016-2275-y

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