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.
@cite_9: Malicious Web sites are a cornerstone of Internet criminal activities. As a result, there has been broad interest in developing systems to prevent the end user from visiting such sites. In this paper, we describe an approach to this problem based on automated URL classification, using statistical methods to discover the tell-tale lexical and host-based properties of malicious Web site URLs. These methods are able to learn highly predictive models by extracting and automatically analyzing tens of thousands of features potentially indicative of suspicious URLs. The resulting classifiers obtain 95-99 accuracy, detecting large numbers of malicious Web sites from their URLs, with only modest false positives.
@cite_11: Conventional wisdom is that phishing represents easy money. In this paper we examine the economics that underly the phenomenon, and find a very different picture. Phishing is a classic example of tragedy of the commons, where there is open access to a resource that has limited ability to regenerate. Since each phisher independently seeks to maximize his return, the resource is over-grazed and yields far less than it is capable of. The situation stabilizes only when the average phisher is making only as much as he gives up in opportunity cost. Since the picture we paint is at variance with accepted wisdom we check against several publicly available data sources on phishing. We find the oft-quoted survey-based estimates of phishing losses unreliable. In particular the victimization rate found in most surveys is smaller than the margin of error, and dollar losses are estimated by averaging unverified self-reported numbers. We estimate that recent public estimates over-state phishing losses by as much as a factor of fifty. This economic portrait illuminates our enemy in an entirely new light. Far from being a path to riches, phishing appears to be a low-skill low-reward business. The enormous amount of phishing activity is evidence of its failure to deliver riches rather than its success, as phishers send more and more email hoping for their share of the bounty that eludes them. Repetition of questionable survey results and unsubstantiated anecdotes makes things worse by ensuring a steady supply of new entrants.
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 brand independence are likely to be poor since they use 100,000 mostly static features (bag-of-words).