A Survey on Phishing Website Detection Using Hadoop

Phishing is an activity carried out by phishers with the aim of stealing personal data of internet users such as user IDs, password, and banking account, that data will be used for their personal interests. Average internet user will be easily trapped by phishers due to the similarity of the websites they visit to the original websites. Because there are several attributes that must be considered, most of internet user finds it difficult to distinguish between an authentic website or not. There are many ways to detecting a phishing website, but the existing phishing website detection system is too time-consuming and very dependent on the database it has. In this research, the focus of Hadoop MapReduce is to quickly retrieve some of the attributes of a phishing website that has an important role in identifying a phishing website, and then informing to users whether the website is a phishing website or not

A Machine Learning Approach for Detection of Phished Websites Using Neural Networks

Phishing is a means of obtaining confidential information through fraudulent website that appear to be legitimate .On detection of all the criteria ambiguities and certain considerations involve hence neural network techniques are used to build an effective tool in identifying phished websites There are many phishing detection techniques available, but a central problem is that web browsers rely on a black list of known phishing website, but some phishing website has a lifespan as short as a few hours. These website with a shorter lifespan are known as zero day phishing website. Thus, a faster recognition system needs to be developed for the web browser to identify zero day phishing website. To develop a faster recognition system, a neural network technique is used which reduces the error and increases the performance. This paper describes a framework to better classify and predict the phishing sites

Experimental Case Studies for Investigating E-Banking Phishing Techniques and Attack Strategies

Phishing is a form of electronic identity theft in which a combination of social engineering and web site spoofing techniques are used to trick a user into revealing confidential information with economic value. The problem of social engineering attack is that there is no single solution to eliminate it completely, since it deals largely with the human factor. This is why implementing empirical experiments is very crucial in order to study and to analyze all malicious and deceiving phishing website attack techniques and strategies. In this paper, three different kinds of phishing experiment case studies have been conducted to shed some light into social engineering attacks, such as phone phishing and phishing website attacks for designing effective countermeasures and analyzing the efficiency of performing security awareness about phishing threats. Results and reactions to our experiments show the importance of conducting phishing training awareness for all users and doubling our efforts in developing phishing prevention techniques. Results also suggest that traditional standard security phishing factor indicators are not always effective for detecting phishing websites, and alternative intelligent phishing detection approaches are needed

The economics of user effort in information security

A significant number of security breaches result from employees' failures to comply with security policies. The cause is often an honest mistake, such as when an employee enters their password in a phishing website, believing it to be a legitimate one.1 It can also be a workaround when faced with an impossible task, such as when an employee has so many different passwords that they must be written down

High Accuracy Phishing Detection Based on Convolutional Neural Networks

The persistent growth in phishing and the rising volume of phishing websites has led to individuals and organizations worldwide becoming increasingly exposed to various cyber-attacks. Consequently, more effective phishing detection is required for improved cyber defence. Hence, in this paper we present a deep learning-based approach to enable high accuracy detection of phishing sites. The proposed approach utilizes convolutional neural networks (CNN) for high accuracy classification to distinguish genuine sites from phishing sites. We evaluate the models using a dataset obtained from 6,157 genuine and 4,898 phishing websites. Based on the results of extensive experiments, our CNN based models proved to be highly effective in detecting unknown phishing sites. Furthermore, the CNN based approach performed better than traditional machine learning classifiers evaluated on the same dataset, reaching 98.2% phishing detection rate with an F1-score of 0.976. The method presented in this pa-per compares favourably to the state-of-the art in deep learning based phishing website detection

DETECTION OF PHISHING WEBSITES USING HYBRID MODEL

Online technologies have revolutionized the modern computing world. Thereare number of users who purchase products online and make payment through variouswebsites. There are multiple websites who ask user to provide sensitive data such asusername, password or credit card details etc. often for malicious reasons. This type ofwebsite is known as phishing website. The phishing website can be detected based on someimportant characteristics like URL (Uniform Resource Locator) and Domain identity.Several approaches have been proposed for detection of phishing websites by extracting thephishing data sets criteria to classify their legitimacy. However, there is no such approachthat can provide better results to the users from phishing attacks. This paper is an attemptto contribute in that area by presenting a hybrid model for classification to detect phishingwebsites with high accuracy and less error rate

Anti-phishing as a web-based user service

This paper describes the recent phenomenon of phishing, in which email messages are sent to unwitting recipients in order to elicit personal information and perpetrate identity theft and financial fraud. A variety of existing techniques for addressing this problem are detailed and a novel approach to the provision of phishing advice is introduced. This takes the form of a Web-based user-service to which users may forward suspect email messages for inspection. The Anti- Phishing Web Service rates the suspect email and provides a Web-based report that the submitter may view. This approach promises benefits in the form of added security for the end-user and insight on the factors that are most revealing of phishing attacks

Intelligent phishing website detection system using fuzzy techniques.

Phishing websites are forged web pages that are created by malicious people to mimic web pages of real websites and it attempts to defraud people of their personal information. Detecting and identifying Phishing websites is really a complex and dynamic problem involving many factors and criteria, and because of the subjective considerations and the ambiguities involved in the detection, Fuzzy Logic model can be an effective tool in assessing and identifying phishing websites than any other traditional tool since it offers a more natural way of dealing with quality factors rather than exact values. In this paper, we present novel approach to overcome the `fuzziness¿ in traditional website phishing risk assessment and propose an intelligent resilient and effective model for detecting phishing websites. The proposed model is based on FL operators which is used to characterize the website phishing factors and indicators as fuzzy variables and produces six measures and criteria¿s of website phishing attack dimensions with a layer structure. Our experimental results showed the significance and importance of the phishing website criteria (URL & Domain Identity) represented by layer one, and the variety influence of the phishing characteristic layers on the final phishing website rate

VisualPhishNet: Zero-Day Phishing Website Detection by Visual Similarity

Phishing websites are still a major threat in today's Internet ecosystem. Despite numerous previous efforts, similarity-based detection methods do not offer sufficient protection for the trusted websites - in particular against unseen phishing pages. This paper contributes VisualPhishNet, a new similarity-based phishing detection framework, based on a triplet Convolutional Neural Network (CNN). VisualPhishNet learns profiles for websites in order to detect phishing websites by a similarity metric that can generalize to pages with new visual appearances. We furthermore present VisualPhish, the largest dataset to date that facilitates visual phishing detection in an ecologically valid manner. We show that our method outperforms previous visual similarity phishing detection approaches by a large margin while being robust against a range of evasion attacks