Phishing as one of the most well-known cybercrime activities is a deception of online users to steal their personal or confidential information by impersonating a legitimate website. Several machine learning-based strategies have been proposed to detect phishing websites. These techniques are dependent on the features extracted from the website samples. However, few studies have actually considered efficient feature selection for detecting phishing attacks. In this work, we investigate an agreement on the definitive features which should be used in phishing detection. We apply Fuzzy Rough Set (FRS) theory as a tool to select most effective features from three benchmarked data sets. The selected features are fed into three often used classifiers for phishing detection. To evaluate the FRS feature selection in developing a generalizable phishing detection, the classifiers are trained by a separate out-of-sample data set of 14,000 website samples. The maximum F-measure gained by FRS feature selection is 95 using Random Forest classification. Also, there are 9 universal features selected by FRS over all the three data sets. The F-measure value using this universal feature set is approximately 93 which is a comparable result in contrast to the FRS performance. Since the universal feature set contains no features from third-part services, this finding implies that with no inquiry from external sources, we can gain a faster phishing detection which is also robust toward zero-day attacks.
Since the rapid advancement in computer networks and ebusiness technologies, massive numbers of sales transactions are performed on the World Wide Web on daily basis. These transactions necessitate online financial payments and the use of ebanking hence attracting phishers to target online users' credentials to access their financial information. Phishing involves developing forged websites that are visually identical to truthful websites in order to deceive online users. Different anti-phishing techniques have been proposed to reduce the risks of phishing mainly by educating users or using automated software. One of the main challenge for automated anti-phishing tools is to determine the more influential features in order to detect phishing activities. This article addresses this problem by conducting a thorough analysis using filtering methods against real phishing websites data. The methodology employed is based on data mining method called RIPPER to determine the performance of the classification systems derived by RIPPER and according to different evaluation measures such as error rate, false positives and false negatives. Besides optimizing classifier predictive performance and addressing the curse of the dimensionality problem, feature selection techniques support a classification model as simple as possible. In this paper, we present a wrapper feature selection approach based on Bat Algorithm (BA) and Optimum-Path Forest (OPF), in which we model the problem of feature selection as an binary-based optimization technique, guided by BA using the OPF accuracy over a validating set as the fitness function to be maximized. Moreover, we present a methodology to better estimate the quality of the reduced feature set. Experiments conducted over six public datasets demonstrated that the proposed approach provides statistically significant more compact sets and, in some cases, it can indeed improve the classification effectiveness. In this paper, we propose a method of phishing website detection that utilizes a meta-heuristic-based nonlinear regression algorithm together with a feature selection approach. In order to validate the proposed method, we used a dataset comprised of 11055 phishing and legitimate webpages, and select 20 features to be extracted from the mentioned websites. This research utilizes two feature selection methods: decision tree and wrapper to select the best feature subset, while the latter incurred the detection accuracy rate as high as 96.32 . After the feature selection process, two meta-heuristic algorithms are successfully implemented to predict and detect the fraudulent websites: harmony search (HS) which was deployed based on nonlinear regression technique and support vector machine (SVM). The nonlinear regression approach was used to classify the websites, where the parameters of the proposed regression model were obtained using HS algorithm. The proposed HS algorithm uses dynamic pitch adjustment rate and generated new harmony. The nonlinear regression based on HS led to accuracy rates of 94.13 and 92.80 for train and test processes, respectively. As a result, the study finds that the nonlinear regression-based HS results in better performance compared to SVM.