Phishing is a major problem on the Web. Despite the significant attention it has received over the years, there has been no definitive solution. While the state-of-the-art solutions have reasonably good performance, they require a large amount of training data and are not adept at detecting phishing attacks against new targets. In this paper, we begin with two core observations: (a) although phishers try to make a phishing webpage look similar to its target, they do not have unlimited freedom in structuring the phishing webpage; and (b) a webpage can be characterized by a small set of key terms; how these key terms are used in different parts of a webpage is different in the case of legitimate and phishing webpages. Based on these observations, we develop a phishing detection system with several notable properties: it is language-independent, can be implemented entirely on client-side, has excellent classification performance and is fast. In addition, we developed a target identification component that can identify the target website that a phishing webpage is attempting to mimic. The target detection component is faster than previously reported systems and can help minimize false positives in our phishing detection system.
We can see that among the eight most relevant state-of-the-art techniques, only two @cite_9 @cite_11 have comparable false positive rates to ours ( @math ). A low false positive rate is paramount for a phishing detection technique, since this relates to the proportion of legitimate webpages to which a user will be incorrectly denied. The technique proposed by Ma @cite_9 has a lower accuracy than in our system ( @math ). In addition, they use a testing set that does not represent real world distribution (3 legs 4 phishs) and use a cross-validation that does not assess scalability of the approach with a 1 1 ratio for learning to testing instances. Whittaker @cite_11 report results similar to us in several metrics. However, they use a and their test set is actually than the training set (a sixth, at 1.5M)! Scalability and language-independence are likely to be poor since they use 100,000 mostly static features (bag-of-words).
@cite_9 @cite_11
2779627157 2788346622 2890718808 1990188874
The existing anti-phishing approaches use the blacklist methods or features based machine learning techniques. Blacklist methods fail to detect new phishing attacks and produce high false positive rate. Moreover, existing machine learning based methods extract features from the third party, search engine, etc. Therefore, they are complicated, slow in nature, and not fit for the real-time environment. To solve this problem, this paper presents a machine learning based novel anti-phishing approach that extracts the features from client side only. We have examined the various attributes of the phishing and legitimate websites in depth and identified nineteen outstanding features to distinguish phishing websites from legitimate ones. These nineteen features are extracted from the URL and source code of the website and do not depend on any third party, which makes the proposed approach fast, reliable, and intelligent. Compared to other methods, the proposed approach has relatively high accuracy in detection of phishing websites as it achieved 99.39 true positive rate and 99.09 of overall detection accuracy. 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. Abstract Due to the rapid growth of the Internet, users change their preference from traditional shopping to the electronic commerce. Instead of bank shop robbery, nowadays, criminals try to find their victims in the cyberspace with some specific tricks. By using the anonymous structure of the Internet, attackers set out new techniques, such as phishing, to deceive victims with the use of false websites to collect their sensitive information such as account IDs, usernames, passwords, etc. Understanding whether a web page is legitimate or phishing is a very challenging problem, due to its semantics-based attack structure, which mainly exploits the computer users’ vulnerabilities. Although software companies launch new anti-phishing products, which use blacklists, heuristics, visual and machine learning-based approaches, these products cannot prevent all of the phishing attacks. In this paper, a real-time anti-phishing system, which uses seven different classification algorithms and natural language processing (NLP) based features, is proposed. The system has the following distinguishing properties from other studies in the literature: language independence, use of a huge size of phishing and legitimate data, real-time execution, detection of new websites, independence from third-party services and use of feature-rich classifiers. For measuring the performance of the system, a new dataset is constructed, and the experimental results are tested on it. According to the experimental and comparative results from the implemented classification algorithms, Random Forest algorithm with only NLP based features gives the best performance with the 97.98 accuracy rate for detection of phishing URLs. Phishing is a plague in cyberspace. Typically, phish detection methods either use human-verified URL blacklists or exploit Web page features via machine learning techniques. However, the former is frail in terms of new phish, and the latter suffers from the scarcity of effective features and the high false positive rate (FP). To alleviate those problems, we propose a layered anti-phishing solution that aims at (1) exploiting the expressiveness of a rich set of features with machine learning to achieve a high true positive rate (TP) on novel phish, and (2) limiting the FP to a low level via filtering algorithms. Specifically, we proposed CANTINA+, the most comprehensive feature-based approach in the literature including eight novel features, which exploits the HTML Document Object Model (DOM), search engines and third party services with machine learning techniques to detect phish. Moreover, we designed two filters to help reduce FP and achieve runtime speedup. The first is a near-duplicate phish detector that uses hashing to catch highly similar phish. The second is a login form filter, which directly classifies Web pages with no identified login form as legitimate. We extensively evaluated CANTINA+ with two methods on a diverse spectrum of corpora with 8118 phish and 4883 legitimate Web pages. In the randomized evaluation, CANTINA+ achieved over 92p TP on unique testing phish and over 99p TP on near-duplicate testing phish, and about 0.4p FP with 10p training phish. In the time-based evaluation, CANTINA+ also achieved over 92p TP on unique testing phish, over 99p TP on near-duplicate testing phish, and about 1.4p FP under 20p training phish with a two-week sliding window. Capable of achieving 0.4p FP and over 92p TP, our CANTINA+ has been demonstrated to be a competitive anti-phishing solution.