A Proactive Approach to Preventing Phishing Attacks Using Pshark

Shah, Ripan; Trevathan, Jarrod; Read, Wayne; Ghodosi, Hossein

doi:10.1109/itng.2009.302

Cited by 13 publications

(9 citation statements)

References 6 publications

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“…Phishing is the process of enticing people into visiting fraudulent websites and persuading them to enter their personal information. Numbers of phishing email are spread with the aim of making web users believe that they are communicating with a trusted entity [2]. Phishing deployed by use advanced technical means.…”

Section: State-of-the-art Of E-mail Phishingmentioning

confidence: 99%

“…A lot of antiphishing techniques are in vogue to draw a dividing line or identify between the fake and the authentic websites, however due to the vast amount and new harmful tactics of phisher, the challenges are yet being faced. [2] [3] For instance, a system can be technically safe and secure enough against password theft, however naive users may leak their sensitive information if an attacker lead them to update their sensitive information such as username, passwords via a given Hypertext Transfer Protocol (HTTP) link [4]. It could ultimately breach the security of the system, web vulnerabilities like obfuscated/phishing URLs can be used by phishers to craft far more influencing socially-engineered messages.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Email Phishing: An Enhanced Classification Model to Detect Malicious URLs

Sankhwar

Pandey

Khan³

2019

ICST Transactions on Scalable Information Systems

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Phishing is the process of enticing people into visiting fraudulent websites and persuading them to enter their personal information. Number in phishing email are spread with the aim of making web users believe that they are communicating with a trusted entity or organization. Phishing is deployed by the use of advanced and harmful tactics like malicious or phishing URLs. So, it becomes necessary to detect malicious or phishing URLs in the present scenario. Numerous antiphishing techniques are in vogue to discriminate fake and the authentic website but are not effective. This research, focuses on the relevant URLs features that discriminate between legitimate and malicious/phishing URLs. The impact of email phishing can be largely reduced by adopting an appropriate combination of all these features with classification techniques. Therefore, an Enhanced Malicious URLs Detection (EMUD) model is developed with machine learning techniques for better classification and accurate results.

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Section: State-of-the-art Of E-mail Phishingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Email Phishing: An Enhanced Classification Model to Detect Malicious URLs

Sankhwar

Pandey

Khan³

2019

ICST Transactions on Scalable Information Systems

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“…Pshark is an approach proposed by Shah et al [19] to detect and eliminate the identified phishing web page from host server. The WHOIS database is used to retrieve the information about the page.…”

Section: Related Workmentioning

confidence: 99%

Intelligent phishing url detection using association rule mining

Jeeva

Rajsingh

2016

Hum. Cent. Comput. Inf. Sci.

107

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Phishing is an online criminal act that occurs when a malicious webpage impersonates as legitimate webpage so as to acquire sensitive information from the user. Phishing attack continues to pose a serious risk for web users and annoying threat within the field of electronic commerce. This paper focuses on discerning the significant features that discriminate between legitimate and phishing URLs. These features are then subjected to associative rule mining—apriori and predictive apriori. The rules obtained are interpreted to emphasize the features that are more prevalent in phishing URLs. Analyzing the knowledge accessible on phishing URL and considering confidence as an indicator, the features like transport layer security, unavailability of the top level domain in the URL and keyword within the path portion of the URL were found to be sensible indicators for phishing URL. In addition to this number of slashes in the URL, dot in the host portion of the URL and length of the URL are also the key factors for phishing URL.

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“…If no anti-phishing tool is installed or the user has not updated the anti-phishing program, then there is no layer of protection. This is referred to as passive antiphishing [16]. It is because the approach only locally protects the user from a phishing attack but does not make any effort to stop or remove the Phisher at the source.…”

Section: Related Work and Literature Reviewmentioning

confidence: 99%

Artificial Immune System Based Classification Approach for Detecting Phishing Mails

avan¹

2015

IJIRCCE

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Phishing/Spam is an attack that deals with social engineering methodology to illegally acquire and use someone else's data on behalf of legitimate website for own benefits. Phishing emails are messages designed to fool the recipient into handing over personal information, such as login names, passwords, credit card numbers, account credentials, social security numbers etc. Fraudulent emails harm their victims through loss of funds and identity theft. They also hurt Internet business, because people lose their trust in Internet transactions for fear that they will become victims of fraud. Filtering approaches using blacklists are not completely effective as about every minute a new phishing scam is created. It has been investigated that the statistical filtering of phishing emails, where a classifier is trained on characteristic features of existing emails and subsequently is able to identify new phishing emails with different contents. This paper deals with the phishing detection problem and how to auto detect phishing emails. The proposed phishing detection model is based on the extracted email features to detect phishing emails, these features appeared in the header and HTML body of email. The developed model introduces Artificial Immune System methodology to classify whether the tested email is phishing or not.

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A Proactive Approach to Preventing Phishing Attacks Using Pshark

Cited by 13 publications

References 6 publications

Email Phishing: An Enhanced Classification Model to Detect Malicious URLs

Email Phishing: An Enhanced Classification Model to Detect Malicious URLs

Intelligent phishing url detection using association rule mining

Artificial Immune System Based Classification Approach for Detecting Phishing Mails

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