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

期刊

NEURAL COMPUTING & APPLICATIONS
卷 28, 期 12, 页码 3629-3654

出版社

SPRINGER LONDON LTD
DOI: 10.1007/s00521-016-2275-y

关键词

Bag-of-word; Data mining; Key logger; Machine learning; Malware; Phishing; Social engineering; Soft computing; Spam; Visual similarity

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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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