User experiences of TORPEDO: TOoltip-poweRed Phishing Email DetectiOn

We propose a concept called TORPEDO to improve phish detection by providing just-in-time and just-in-place trustworthy tooltips. These help people to identify phish links embedded in emails. TORPEDO's tooltips contain the actual URL with the domain highlighted. Link activation is delayed for a short period, giving the person time to inspect the URL before they click on a link. Furthermore, TORPEDO provides an information diagram to explain phish detection. We evaluated TORPEDO's effectiveness, as compared to the worst case “status bar” as provided by other Web email interfaces. People using TORPEDO performed significantly better in detecting phishes and identifying legitimate emails (85.17% versus 43.31% correct answers for phish). We then carried out a field study with a number of TORPEDO users to explore actual user experiences of TORPEDO. We conclude the paper by reporting on the outcome of this field study and suggest improvements based on the feedback from the field study participants

Real Time and Continuous Detection of Phishing Websites

Web Spoofing draws the client to associate with the fake sites instead of the genuine ones. The fundamental goal of this assault is to take the delicate data from the clients. The assailant makes a "shadow" site that seems to be like the genuine site. This deceitful demonstration enables the aggressor to watch and adjust any data from the client. This paper proposes a discovery system of phishing sites in view of checking Uniform Resources Locators (URLs) of pages. The proposed arrangement can recognize the real site page and fake website page by checking the Uniform Resources Locators (URLs) of suspected pages. URLs are examined in light of specific qualities to check the phishing website pages. The identified assaults are accounted for aversion. The execution of the proposed arrangement is assessed utilizing Phistank and Yahoo catalog datasets. The got comes about demonstrate that the location instrument is deployable and competent to distinguish different sorts of phishing assaults keeping up a low rate of false alerts

A Survey of Website Phishing Detection Techniques

This article surveys the literature on website phishing detection. Web Phishing lures the user to interact with the fake website. The main objective of this attack is to steal the sensitive information from the user. The attacker creates similar website that looks like original website. It allows attacker to obtain sensitive information such as username, password, credit card details etc. This paper aims to survey many of the recently proposed website phishing detection techniques. A high-level overview of various types of phishing detection techniques is also presented

Categorization of Phishing Detection Features And Using the Feature Vectors to Classify Phishing Websites

abstract: Phishing is a form of online fraud where a spoofed website tries to gain access to user's sensitive information by tricking the user into believing that it is a benign website. There are several solutions to detect phishing attacks such as educating users, using blacklists or extracting phishing characteristics found to exist in phishing attacks. In this thesis, we analyze approaches that extract features from phishing websites and train classification models with extracted feature set to classify phishing websites. We create an exhaustive list of all features used in these approaches and categorize them into 6 broader categories and 33 finer categories. We extract 59 features from the URL, URL redirects, hosting domain (WHOIS and DNS records) and popularity of the website and analyze their robustness in classifying a phishing website. Our emphasis is on determining the predictive performance of robust features. We evaluate the classification accuracy when using the entire feature set and when URL features or site popularity features are excluded from the feature set and show how our approach can be used to effectively predict specific types of phishing attacks such as shortened URLs and randomized URLs. Using both decision table classifiers and neural network classifiers, our results indicate that robust features seem to have enough predictive power to be used in practice.Dissertation/ThesisMasters Thesis Computer Science 201