Textual And Visual Content Based Anti Phishing A Bayesian Approach Pdf
File Name: textual and visual content based anti phishing a bayesian approach .zip
Metrics details. Phishing is a technique under Social Engineering attacks which is most widely used to get user sensitive information, such as login credentials and credit and debit card information, etc.
- Development of anti-phishing browser based on random forest and rule of extraction framework
- Textual and Visual Content-Based Anti-Phishing: A Bayesian Approach
- Phishing Detection with Popular Search Engines: Simple and Effective
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Development of anti-phishing browser based on random forest and rule of extraction framework
Ankit Kumar Jain, B. Phishing is one of the major problems faced by cyber-world and leads to financial losses for both industries and individuals. Detection of phishing attack with high accuracy has always been a challenging issue. At present, visual similarities based techniques are very useful for detecting phishing websites efficiently.
Phishing website looks very similar in appearance to its corresponding legitimate website to deceive users into believing that they are browsing the correct website. Visual similarity based phishing detection techniques utilise the feature set like text content, text format, HTML tags, Cascading Style Sheet CSS , image, and so forth, to make the decision.
These approaches compare the suspicious website with the corresponding legitimate website by using various features and if the similarity is greater than the predefined threshold value then it is declared phishing. This paper presents a comprehensive analysis of phishing attacks, their exploitation, some of the recent visual similarity based approaches for phishing detection, and its comparative study.
Our survey provides a better understanding of the problem, current solution space, and scope of future research to deal with phishing attacks efficiently using visual similarity based approaches.
Phishing is a crime in which a perpetrator sends the fake e-mail, which appears to come from popular and trusted brand or organization, asking to input personal credential like bank password, username, phone number, address, credit card details, and so forth [ 1 — 4 ]. The fake e-mails often look amazingly legitimate, and even the website where the Internet user is asked to input personal information also looks similar to legitimate one.
Moreover, spear phishing attack is becoming popular nowadays. Business e-mail compromise BEC is observed as a major Internet threat in [ 6 ]. In BEC, the intruder uses spear phishing methods to fool organizations and Internet persons. More sophisticated spear phishing attacks [ 7 — 9 ] targeted particular individual or groups within the organization. When a user opens a fake webpage and enters the username and protected password, the credentials of the user are acquired by the attacker which can be used for malicious purposes [ 12 — 22 ].
Phishing websites look very similar in appearance to their corresponding legitimate websites to attract large number of Internet users. Recent developments in phishing detection have led to the growth of numerous new visual similarity based approaches. Visual similarity based approaches compare the visual appearance of the suspicious website to its corresponding legitimate website by using various parameters.
Due to different phases of phishing detection, this paper contains the following: i Background, History, and Statistics section presents the history of phishing attacks, worldwide financial losses due to phishing attacks, the lifecycle of phishing attack, and classification of various types of phishing attacks. This section describes the overall picture of phishing attacks from a high level perspective.
Moreover, we present a comparison between various visual similarity based antiphishing techniques. It provides a better understanding of the problem, current solution space, and future research scope to efficiently deal with phishing attacks using visual similarity based approach.
The rest of the paper is structured as follows. Section 2 contains the background, history, and statistics of phishing attack. Section 3 describes the overview of phishing detection using visual similarity based approaches. Section 4 presents the taxonomy of various types of phishing detection and filtering techniques; especially this section focuses on visual similarity approaches in detail.
Section 5 presents the performance and evaluation matrices to judge the antiphishing system. Section 6 presents the open issues and challenges in phishing detection and protection. Finally, Section 7 concludes the paper. A phishing scam has attracted the attention of both academicians and corporate researchers as it is a serious privacy and web security threat [ 23 — 33 ].
Phishing cannot be controlled by firewalls or any encryption software [ 34 — 36 ]. First phishing attack was observed on America online network systems AOL in the early s [ 37 ] where many fraudulent users registered on AOL website with fake credit card details. AOL passed these fake accounts with a simple validity test without verifying the legitimacy of the credit card. After activation of the fake account, attackers accessed the resources of America online system.
At the time of billing, AOL determined that the accounts were fraudulent, and associated credit cards were also not valid; therefore AOL ceased these accounts immediately. After this incident, AOL took measures to prevent this type of attack by verifying the authenticity of credit card and associated billing identity, which also enabled the attackers to change their way of obtaining AOL accounts.
Instead of creating a fake account, attackers would steal the personal information of registered AOL user. Attackers contacted registered AOL users through instant messenger or e-mail and asked them to verify the password for security purposes.
E-mail and instant messages appeared to come from an AOL employee. Many users provided their passwords and other personal information to the attackers. The attackers then used the variously billed portions of America online website on behalf of a legitimate user.
Moreover, an attacker no longer restricts themselves to masquerading America online website but actively masquerade a large number of financial and electronic commerce websites. According to Internet world stats [ 38 ], total numbers of Internet users worldwide are 2.
Hackers take advantage of the insecure Internet system and can fool unaware users to fall for phishing scams. Phishing e-mail is used to defraud both individuals and financial organizations on the Internet.
The Anti-Phishing Working Group APWG [ 39 ] is an international consortium which is dedicated to promoting research, education, and law enforcement to eliminate online fraud and cyber-crime. The total phishing attacks detected in were approximately and led to financial losses more than 5. The total number of phishing attacks noticed in Q1 first quarter of was ,, a According to the APWG report in the first quarter of , second highest number of phishing attacks ever recorded was between January and March [ 40 ] and payment services are the most targeted industry.
During the second half of , , unique phishing attacks were observed [ 41 ]. In the year , total financial losses were 1.
The financial losses due to phishing attack in and were 4. The growth of phishing attacks from to is shown in Figure 2. The phishing mechanism is shown in Figure 3. The fake website is the clone of targeted genuine website, and it always contains some input fields e. An attacker steals the credential of the innocent user by performing following steps:. Construction of Phishing Site. In the first step attacker identifies the target as a well-known organization.
Afterward, attacker collects the detailed information about the organization by visiting their website. The attacker then uses this information to construct the fake website. URL Sending. In this step, attacker composes a bogus e-mail and sends it to the thousands of users. Attacker attached the URL of the fake website in the bogus e-mail. In the case of spear phishing attack, an attacker sends the e-mail to selected users.
An attacker can also spread the link of phishing website with the help of blogs, forum, and so forth [ 43 ]. Stealing of the Credentials.
When user clicks on attached URL, consequently, fake site is opened in the web browser. The fake website contains a fake login form which is used to take the credential of an innocent user. Furthermore, attacker can access the information filled by the user. Identity Theft. Attacker uses this credential of malicious purposes. For example, attacker purchases something by using credit card details of the user.
Attacker performed the phishing attack by utilising the technical subterfuge and social engineering techniques [ 40 , 44 ]. In social engineering techniques, attackers carry out this attack by sending bogus e-mail.
Attackers often convince recipients to respond using names of banks, credit card companies, e-retailers, and so forth [ 45 ]. The malware also misaddresses users to fake websites or proxy servers. Classification of various phishing attacks is shown in Figure 4. A phishing scam starts with spreading bogus e-mail.
After receiving an e-mail, antiphishing techniques start working, either by redirecting the phishing mail in the spam folder or by showing a warning when an online user clicks on the link of phishing URL. The lifecycle of phishing attack is shown in Figure 5.
The following steps are involved in phishing lifecycle:. Step 1. Attacker creates the fake copy of a popular organization and sends the URL of fake website to the large number of Internet users using e-mail, blog, social networking sites, and so forth. Step 2. In the case of fake e-mail, every e-mail is first to pass through the DNS-based blacklist filters. If the domain is found in the blacklist, then e-mail is blocked before it reached to SMTP mail server. There are also various solutions available which block the fake e-mail based on structural features of mail [ 44 ].
Step 3. If a fake e-mail bypasses the blacklist and features based solutions and if the user opens attached link in the e-mail then some browser based blacklist techniques block the site at client side.
Step 4. Some other solutions like the heuristic and visual similarities based approaches also blocked the webpage only when the browser requests for any suspicious webpage. Step 5. If the phishing attack bypasses all the solutions then it steals the credential of innocent users and sends it to the attacker. The attacker uses this information for financial or some other benefits.
A user could become the victim of the phishing attack by looking the high visual resemblance of phishing website with the targeted legitimate site, such as page layouts, images, text content, font size, and font colour. The fake and genuine webpages of PayPal are shown in Figure 6 , and both pages have same visual appearance but different URLs.
If an attacker does not copy the visual appearance of targeted website well, then chances of inputting credentials by Internet users are very less. An attacker fools the user by the following ways: 1 Visual Appearance.
Textual and Visual Content-Based Anti-Phishing: A Bayesian Approach
David G. J F Nunamaker, Jr. Roger H. James V. Hansen J. Victor A. Kai R.
Textual and Visual Content-Based Anti-Phishing: A Bayesian Approach. Haijun Zhang, Gang Liu, Tommy W. S. Chow, Senior Member, IEEE, and Wenyin Liu.
Phishing Detection with Popular Search Engines: Simple and Effective
We propose a new phishing detection heuristic based on the search results returned from popular web search engines such as Google, Bing and Yahoo. The full URL of a website a user intends to access is used as the search string, and the number of results returned and ranking of the website are used for classification. Unable to display preview.
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