Phishing attack is a cybercrime that can lead to severe financial losses for Internet users and entrepreneurs. Typically, phishers are fond of using fuzzy techniques during the creation of a website. They confuse the victim by imitating the appearance and content of a legitimate website. In addition, many websites are vulnerable to phishing attacks, including financial institutions, social networks, e-commerce, and airline websites. This paper is an extension of our previous work that leverages the favicon with Google image search to reveal the identity of a website. Our identity retrieval technique involves an effective mathematical model that can be used to assist in retrieving the right identity from the many entries of the search results. In this paper, we introduced an enhanced version of the favicon-based phishing attack detection with the introduction of the Domain Name Amplification feature and incorporation of addition features. Additional features are very useful when the website being examined does not have a favicon. We have collected a total of 5,000 phishing websites from PhishTank and 5,000 legitimate websites from Alexa to verify the effectiveness of the proposed method. From the experimental results, we achieved a 96.93% true positive rate with only a 4.13% false positive rate.

Anti-phishing research is one of the active research fields in information security. Due to the lack of a publicly accessible standard test dataset, most of the researchers are using their own dataset for the experiment. This makes the benchmarking across different anti-phishing techniques become challenging and inefficient. In this paper, we propose and construct a large-scale standard offline dataset that is downloadable, universal and comprehensive. In designing the dataset creation approach, major anti-phishing techniques from the literature have been thoroughly considered to identify their unique requirements. The findings of this requirement study have concluded several influencing factors that will enhance the dataset quality, which includes: the type of raw elements, source of the sample, sample size, website category, category distribution, language of the website and the support for feature extraction. These influencing factors are the core to the proposed dataset construction approach, which produced a collection of 30,000 samples of phishing and legitimate webpages with a distribution of 50 percent of each type. Thus, this dataset is useful and compatible for a wide range of anti-phishing researches in conducting the benchmarking as well as beneficial for a research to conduct a rapid proof of concept experiment. With the rapid development of anti-phishing research to counter the fast evolution of phishing attacks, the need of such dataset cannot be overemphasised. The complete dataset is available for download at http://www.fcsit.unimas.my/research/legit-phish-set. Â

Quick response (QR) code gained popularity and has been adapted for various applications such as a pointer to digital information and authentication. While the code gives convenience as a physical pointer to the digital world, it can be manipulated to divert the intended destination of the link to a malicious site. Thus, QR codes can be easily exploited by phishers to launch phishing attacks. Here, the current phishing attacks that utilise the QR code as a vector are surveyed and categorised. The recent countermeasures for such attacks are surveyed as well. It is also found that, current countermeasures are insufficient and face challenges like barcode-in-barcode attacks, high overhead solutions and limited data space in the code. In comparison to the amount of work done in web and email phishing detection, QR code phishing detection still inadequate. This paper hopes to shed light on the recent phishing attacks using QR code and the countermeasures proposed to tackle these attacks.