Anti-Phishing Models: Main Challenges

Phishing is a form of online identity theft in which the attacker attempts to fraudulently retrieve a legitimate user\u27s account information, logon credentials or identity information in general. The compromised information is then used for withdrawing money online, taking out cash advances, or making purchases of goods and services on the accounts. Various solutions have been proposed and developed in response to phishing. As phishing is a business problem, the solutions target both non-technical and technical areas. This paper investigates the current anti-phishing solutions and critically reviews their usage, security weaknesses and their effectiveness. The analysis of these models points to a conclusion that technology alone will not completely stop phishing. What is necessary is a multi-tiered, organised approach: user awareness, technical and non-technical solutions should work together

Detecting Phishing Websites Using Associative Classification

Phishing is a criminal technique employing both social engineering and technical subterfuge to steal consumer's personal identity data and financial account credential. The aim of the phishing website is to steal the victims’ personal information by visiting and surfing a fake webpage that looks like a true one of a legitimate bank or company and asks the victim to enter personal information such as their username, account number, password, credit card number, …,etc. This paper main goal is to investigate the potential use of automated data mining techniques in detecting the complex problem of phishing Websites in order to help all users from being deceived or hacked by stealing their personal information and passwords leading to catastrophic consequences. Experimentations against phishing data sets and using different common associative classification algorithms (MCAR and CBA) and traditional learning approaches have been conducted with reference to classification accuracy. The results show that the MCAR and CBA algorithms outperformed SVM and algorithms. Keywords: Phishing Websites, Data Mining, Associative Classification, Machine Learnin

Anti-Phishing Models: Main Challenges

Phishing is a form of online identity theft in which the attacker attempts to fraudulently retrieve a legitimate user\u27s account information, logon credentials or identity information in general. The compromised information is then used for withdrawing money online, taking out cash advances, or making purchases of goods and services on the accounts. Various solutions have been proposed and developed in response to phishing. As phishing is a business problem, the solutions target both non-technical and technical areas. This paper investigates the current anti-phishing solutions and critically reviews their usage, security weaknesses and their effectiveness. The analysis of these models points to a conclusion that technology alone will not completely stop phishing. What is necessary is a multi-tiered, organised approach: user awareness, technical and non-technical solutions should work together

Client Side Script Phishing Attacks Detection Method using Active Content Popularity Monitoring

The phisher can attack the client side script by means of threatening information which affects the majority of online users in sequence. The malicious users steal a variety of sensitive information from financial organizations in order to run nameless client side script in the phishing attack. In most of the time, the consumer will ignore association script and popup windows which in turn run a set of malicious processes and send the sensitive information to the remote sites. To secure consumers by limiting the client side script, an effective Client Side Script Phishing Attack Detection (CSSPAD) method is proposed to detect the client side script phishing attacks. The proposed methodis based on Active Content Popularity Monitoring (ACPM) and client script classification methods. This method categorizes the client side script according to a mixture of factors like the quantity of information being transferred by the script, the parent information of the script is being accessed. The proposed method computes the active time of the script, amount of data transferred and popularity of the webpage

Detecting Phishing Websites Using Associative Classification

Phishing is a criminal technique employing both social engineering and technical subterfuge to steal consumer's personal identity data and financial account credential. The aim of the phishing website is to steal the victims’ personal information by visiting and surfing a fake webpage that looks like a true one of a legitimate bank or company and asks the victim to enter personal information such as their username, account number, password, credit card number, …,etc. This paper main goal is to investigate the potential use of automated data mining techniques in detecting the complex problem of phishing Websites in order to help all users from being deceived or hacked by stealing their personal information and passwords leading to catastrophic consequences. Experimentations against phishing data sets and using different common associative classification algorithms (MCAR and CBA) and traditional learning approaches have been conducted with reference to classification accuracy. The results show that the MCAR and CBA algorithms outperformed SVM and algorithms. Keywords: Phishing Websites, Data Mining, Associative Classification, Machine Learning

TRAWL: Protection against rogue sites for the masses

The number of smartphones reached 3.4 billion in the third quarter of 2016 [1]. These devices facilitate our daily lives and have become the primary way of accessing the web. Although all desktop browsers filter rogue websites, their mobile counterparts often do not filter them at all, exposing their users to websites serving malware or hosting phishing attacks. In this paper we revisit the anti-phishing filtering mechanism which is offered in the most popular web browsers of Android, iOS and Windows Phone. Our results show that mobile users are still unprotected against phishing attacks, as most of the browsers are unable to filter phishing URLs. Thus, we implement and evaluate TRAWL (TRAnsparent Web protection for alL), as a cost effective security control that provides DNS and URL filtering using several blacklists

Emerging Phishing Trends and Effectiveness of the Anti-Phishing Landing Page

Each month, more attacks are launched with the aim of making web users believe that they are communicating with a trusted entity which compels them to share their personal, financial information. Phishing costs Internet users billions of dollars every year. Researchers at Carnegie Mellon University (CMU) created an anti-phishing landing page supported by Anti-Phishing Working Group (APWG) with the aim to train users on how to prevent themselves from phishing attacks. It is used by financial institutions, phish site take down vendors, government organizations, and online merchants. When a potential victim clicks on a phishing link that has been taken down, he / she is redirected to the landing page. In this paper, we present the comparative analysis on two datasets that we obtained from APWG's landing page log files; one, from September 7, 2008 - November 11, 2009, and other from January 1, 2014 - April 30, 2014. We found that the landing page has been successful in training users against phishing. Forty six percent users clicked lesser number of phishing URLs from January 2014 to April 2014 which shows that training from the landing page helped users not to fall for phishing attacks. Our analysis shows that phishers have started to modify their techniques by creating more legitimate looking URLs and buying large number of domains to increase their activity. We observed that phishers are exploiting ICANN accredited registrars to launch their attacks even after strict surveillance. We saw that phishers are trying to exploit free subdomain registration services to carry out attacks. In this paper, we also compared the phishing e-mails used by phishers to lure victims in 2008 and 2014. We found that the phishing e-mails have changed considerably over time. Phishers have adopted new techniques like sending promotional e-mails and emotionally targeting users in clicking phishing URLs

Indirect Financial Loss of Phishing to Global Market

This research studies the indirect financial impact of phishing announcements on firm value. Using about 3,000 phishing announcements, we showed that phishing has a significantly negative impact on firms regardless of their size. We also discovered that place of incorporation, type of ownership, industry, and time are significant factors exacerbating the impact. Our research findings may give some insights to industrial practitioners about attitude of investors towards phishing. Compared to other similar event studies, our research has also made several significant breakthroughs. Firstly, we used the largest data set ever in prior event studies. Secondly, our research is the first to analyze global phenomena concerning phishing. Thirdly, we enhanced the robustness of a regression model by introducing the criterion of selection of best fit market index based on R square. We believe that our research can add value to the literature in the subjects of phishing research and event studies

An Emerging Solution for Detection of Phishing Attacks

In this era of computer age, as more and more people use internet to carry out their day to day work so as hackers performs various security attacks on web browsers and servers to steal user’s vital data. Now Electronic mail (E-mail) is used by everyone including organizations, agency and becoming official communication for the society as a whole in day to day basis. Even though a lot of modern techniques, tools and prevention methods are being developed to secure the users vital information but still they are prone to security attacks by the fraudsters. Phishing is one such attack and its detection with high accuracy is one of the prominent research issues in the area of cyber security. Phisher fraudulently acquire confidential information like user-id, passwords, visa card and master card details through various social engineering methods. Mostly blacklist based methodology is used for detection of phishing attacks but this method has a limitation that it cannot be used for detection of white listed phishing. This chapter aims to use machine learning algorithms to classify between phishing E-mails and genuine E-mails and helps the user in detecting attacks. The architectural model proposed in this chapter is to identify phishing and use J48 decision tree classifier to classify the fake E-mail from real E-mail. The algorithm presented here goes through several stages to identify phishing attack and helps the user in a great way to protect their vital information