Counterfeit Websites Research Articles

Phishing Website Detection using Machine Learning

Phishing is a common attack on credulous people by making them to disclose their unique information using counterfeit websites. The objective of phishing website URLs is to purloin the personal information like user name, passwords and online banking transactions. Phishers use the websites which are visually and semantically similar to those real websites. As technology continues to grow, phishing techniques started to progress rapidly and this needs to be prevented by using anti-phishing mechanisms to detect phishing. Machine learning is a powerful tool used to strive against phishing attacks. In this article, we proposed 5 different algorithms in machine learning to analyse the URLs. The accuracy of the Existing method is approximately 94%, and we have implemented it as 95.235% in the Proposed method. Here we used 5 classifiers which are Random Forest Classifier, AdaBoost Classifier, XGBoost Classifier, Support Vector Machine, Gradient Boosting Classifier. Among all these Classifiers, Random Forest Classifier gives the highest accuracy.

An Intelligent Based Symmetrical Classification of Online Shop Selling Counterfeit Products

In recent years, the social network has become popular and people have started trading transactions on the Internet. Many counterfeit websites have begun to appear which create websites with counterfeit products or use the digital advertiser’s services to promote their websites on social media. Malicious sellers disguise high-quality products to attract consumers since buyers cannot receive transparent information. If there is asymmetry information, a secondary market will be formed. To solve the above problems, this research explored the machine-learning-based method to classify counterfeit and legitimate websites with symmetry information. The data set is 1612 websites used in this paper and a total of 15 feature values and takes 804 counterfeit websites and 808 legitimate websites. The Random Forest and Deep Neural Network algorithms were used to classify fake websites. This study also used statistical tests, such as Chi-square and ANOVA detection, to compare the importance of features in feature selection. The experiment results show that the RF accuracy is 99.2% and the DNN accuracy is 93.2%. The RF Precision and Recall are 100% and 98.5%, respectively. The DNN Precision and Recall are less than RF. Then, the RF F1-score is 99.2% which is higher than DNN.

Machine Learning in Policing Counterfeit Websites

Machine Learning in Policing Counterfeit Websites

Phishing Websites Detection using Machine Learning

Phishing is a common attack on credulous people by making them to disclose their unique information using counterfeit websites. The objective of phishing website URLs is to purloin the personal information like user name, passwords and online banking transactions. Phishers use the websites which are visually and semantically similar to those real websites. As technology continues to grow, phishing techniques started to progress rapidly and this needs to be prevented by using anti-phishing mechanisms to detect phishing. Machine learning is a powerful tool used to strive against phishing attacks. This paper surveys the features used for detection and detection techniques using machine learning

PDRCNN: Precise Phishing Detection with Recurrent Convolutional Neural Networks

Through well-designed counterfeit websites, phishing induces online users to visit forged web pages to obtain their private sensitive information, e.g., account number and password. Existing antiphishing approaches are mostly based on page-related features, which require to crawl content of web pages as well as accessing third-party search engines or DNS services. This not only leads to their low efficiency in detecting phishing but also makes them rely on network environment and third-party services heavily. In this paper, we propose a fast phishing website detection approach called PDRCNN that relies only on the URL of the website. PDRCNN neither needs to retrieve content of the target website nor uses any third-party services as previous approaches do. It encodes the information of an URL into a two-dimensional tensor and feeds the tensor into a novelly designed deep learning neural network to classify the original URL. We first use a bidirectional LSTM network to extract global features of the constructed tensor and give all string information to each character in the URL. After that, we use a CNN to automatically judge which characters play key roles in phishing detection, capture the key components of the URL, and compress the extracted features into a fixed length vector space. By combining the two types of networks, PDRCNN achieves better performance than just using either one of them. We built a dataset containing nearly 500,000 URLs which are obtained through Alexa and PhishTank. Experimental results show that PDRCNN achieves a detection accuracy of 97% and an AUC value of 99%, which is much better than state-of-the-art approaches. Furthermore, the recognition process is very fast: on the trained PDRCNN model, the average per URL detection time only cost 0.4 ms.

A URL with Image-based Feature Extraction for Preventing Phishing Attacks

Currently, Phishing is a type of attack in which cyber criminals tricks the victims to steal their personal and financial data. It has become an organized criminal activity. Spoofed emails claiming to be from legitimate source are crafted in a way to lead victims to reveal their personal, financial data by misdirecting them to the counterfeit website. We compared previous password protection solutions, some of the presented solutions use specialized equipment or additional servers to protect passwords. Other solutions are prone to spoofing and phishing attacks as well as introduce usability issues. Also these solutions do not address the challenge of protecting passwords against the adversary who can, for instance, exploit server-side software vulnerabilities. Our goal is enhancing the best solution to prevent phishing by alerting the users from phishing websites when detected based on URL with image-based feature extraction method.

English

Phishing is a term used to describe various scams that use fraudulent e-mail messages, sent by criminals, to trick people into exposing personal information. The criminals use this information identity to rob bank accounts, or take over computers. Counterfeit web sites, using hijacked company brands and logos are created to lure individuals into revealing information that are confidential. These digital thugs are for any data they can obtain to prey on people and further their criminal activities. E-mail has become an invaluable communication tool, both for business and personal use. Among the many security issues that affect computer users, there is a rapidly growing threat known as phishing. Criminals use phishing attacks to lure the unsuspecting into visiting a fraudulent web site, calling a fraudulent phone number, or downloading malicious software, expressly to steal sensitive information such as credit card numbers, account credentials, social security numbers, PINS, or passwords.

A second chance for authors of hijacked journals to publish in legitimate journals.

This article proposes the republication of articles that have previously been published in counterfeit websites of hijacked journals. The paper also discusses the technical and ethical aspects of republishing such articles.

Preventing Phishing Attacks using One Time Password and User Machine Identification

Phishing is a type of attack in which cyber criminals tricks the victims to steal their personal and financial data. It has become an organized criminal activity. Spoofed emails claiming to be from legitimate source are crafted in a way to lead victims to reveal their personal, financial data by misdirecting them to the counterfeit website. This research paper presents a novel approach to combat the Phishing attacks. An approach is proposed where user will retrieve the one time password by SMS or by alternate email address. After receiving the one time password the web server will create an encrypted token for the users computer or device for authentication. The encrypted token will be used for identification, any time user wishes to access the website he or she must request the new password. The one time password as name implies will expire after single use. The one time password and encrypted token is a smart way to tackle this problem.

Control of spam: a comparative approach with special reference to India

Spam and other related online threats are a real concern to all Internet users as they can lead to online fraud involving counterfeit websites, the theft of personal data such as credit card information, personal information through illicit access to computer systems and false or misleading representations in the market place. These are not imaginary threats but are supported by empirical evidence. This wastes time to users opening spam with misleading subject lines, wastes Internet resources trying to block it, consumes storage and bandwidth, and costs money in fighting it. E-commerce cannot flourish in an uncertain environment where security of personal data is not guaranteed. With the aim of boosting confidence in online commerce and protecting the interests of consumers, governments have introduced anti-spam legislation to protect consumers and businesses. This legislation has introduced different parameters. However, legislation alone cannot prevent spam. It is to be combined with technological measures. There is no separate legislation in India to deal with this problem. This paper analyses technological solutions and legal measures in the light of trans-national developments to provide background for any judicial decision making or legislative policy in India.

Reeling in Big Phish with a Deep MD5 Net

Phishing continues to grow as phishers discover new exploits and attack vectors for hosting malicious content; the traditional response using takedowns and blacklists does not appear to impede phishers significantly. A handful of law enforcement projects — for example the FBI's Digital PhishNet and the Internet Crime and Complaint Center (ic3.gov) — have demonstrated that they can collect phishing data in substantial volumes, but these collections have not yet resulted in a significant decline in criminal phishing activity. In this paper, a new system is demonstrated for prioritizing investigative resources to help reduce the time and effort expended examining this particular form of online criminal activity. This research presents a means to correlate phishing websites by showing that certain websites are created by the same phishing kit. Such kits contain the content files needed to create the counterfeit website and often contain additional clues to the identity of the creators. A clustering algorithm is presented that uses collected phishing kits to establish clusters of related phishing websites. The ability to correlate websites provides law enforcement or other potential stakeholders with a means for prioritizing the allocation of limited investigative resources by identifying frequently repeating phishing offenders.

Blending technology and the law for cyber security [Women to Watch

One day last summer, Jody Westby, chief executive officer of Global Cyber Risk, a consulting company in Washington, D.C., that helps corporations and governments safeguard their online information and networks, received a call from a former government minister. The minister told her that he had given his name and password to what turned out to be an online fraud scheme, and now he was worried. Westby, who is a lawyer as well as a cyber security and privacy expert, wasn't terribly surprised. Many computer fraud schemes no longer look like what you may remember from the Nigerian letter scam--those fishy e-mails in which someone impersonating a foreign government official asks for help getting money to an overseas bank account. Today, says Westby, counterfeit Web sites and e-mail scams can "look good."

Caught in the net [Internet and e-mail security issues

Harvesting bank accounts is frighteningly easy. Millions of pounds are stolen each year, and the authorities seem powerless to prevent it. This article examines various methods used to obtain personal details from unsuspecting users. The methods considered include: phishing - an e-mail directing users to a counterfeit website; pharming - harvesting user details using malicious code installed on their machine; and DNS poisoning - hijacking of the domain name server to redirect the browser to sites other than that one displayed in the address bar. Countermeasures against these threats are discussed in each case.