Phishing has become a serious cyber-security issue, and it is spreading through various media such as e-mail, SMS to capture the victim’s critical profile information. Although many novel anti-phishing techniques have been developed to forestall the progress of phishing, it remains an unresolved issue. Smishing is an incarnation of Phishing attack, which utilizes Short Messaging Service (SMS) or simple text message on mobile phones to lure the victim’s online credentials. This paper presents an anti-phishing model entitled ‘SmiDCA’ (SMIshing Detection based on Correlation Algorithm). The proposed model has collected different smishing messages from various sources, and 39 distinct features were extracted initially. The SmiDCA model incorporates dimensionality reduction, and machine Learning-based experiments were conducted on without (BFSA) and with (AFSA) reduction of features. The model has been validated with experiments on both the English and non-English datasets and the results of both of these experiments are encouraging in terms of accuracy: 96.40% for English dataset and 90.33% for the non-English dataset. In addition, the model achieved an accuracy of 96.16% even after nearly half of the features were pruned.

Phishing has appeared as a critical issue in the cybersecurity domain. Phishers adopt email as one of their major channels of communication to lure potential victims. This paper attempts to detect phishing emails by using binary search feature selection (BSFS) with a Pearson correlation coefficient algorithm as a ranking method. The proposed method utilizes four sets of features from the email subject, the body of the email, hyperlinks, and readability of contents. Overall, 41 features were selected from the aforementioned four dimensions. The result shows that the BSFS method evaluated the accuracy of 97.41% in comparison with SFFS (95.63%) and WFS (95.56%). This exploration shows that the SFFS requires more time to ascertain the optimum features set and the WFS requires the least time; however, the accuracy of WFS is very low in comparison with other algorithms. The significant finding of the experiment is that the BFSF requires the least time to evaluate the best feature set with better accuracy even though few features are removed from the feature corpus.

The SMS phishing is another method where the phisher operates the SMS as a medium to communicate with the victims and this method is identified as smishing (SMS + phishing). Researchers promoted several anti-phishing methods where the correlation algorithm is applied to explore the relevancy of the features since there are numerous features in the features corpus. The correlation algorithm assesses the rank of the features that is the highest rank leads to the more relevant to the appropriate assignment. Therefore, this paper analyses four rank correlation algorithms particularly Pearson rank correlation, Spearman’s rank correlation, Kendall rank correlation, and Point biserial rank correlation with a machine-learning algorithm to determine the best features set for detecting Smishing messages. The result of the investigation reveals that the AdaBoost classifier offered better accuracy. Further analysis shows that the classifier with the ranking algorithm that is Kendall rank correlation appeared superior accuracy than the other correlation algorithms. The inferred of this experiment confirms that the ranking algorithm was able to reduce the dimension of features with 61.53% and presented an accuracy of 98.40%.

Purpose: This paper aims to propose a model entitled MMSPhiD (multidimensional similarity metrics model for screen reader user to phishing detection) that amalgamates multiple approaches to detect phishing URLs.Design/methodology/approach: The model consists of three major components: machine learning-based approach, typosquatting-based approach and phoneme-based approach. The major objectives of the proposed model are detecting phishing URL, typosquatting and phoneme-based domain and suggesting the legitimate domain which is targeted by attackers.Findings: The result of the experiment shows that the MMSPhiD model can successfully detect phishing with 99.03 per cent accuracy. In addition, this paper has analyzed 20 leading domains from Alexa and identified 1,861 registered typosquatting and 543 phoneme-based domains.Research limitations/implications: The proposed model has used machine learning with the list-based approach. Building and maintaining the list shall be a limitation.Practical implication: The results of the experiments demonstrate that the model achieved higher performance due to the incorporation of multi-dimensional filters.Social implications: In addition, this paper has incorporated the accessibility needs of persons with visual impairments and provides an accessible anti-phishing approach.Originality/value: This paper assists persons with visual impairments on detection phoneme-based phishing domains.