The Experts below are selected from a list of 9 Experts worldwide ranked by ideXlab platform
Jaber Alwedyan - One of the best experts on this subject based on the ideXlab platform.
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Detecting Phishing Websites Using Associative Classification
2016Co-Authors: Al Ajlouni, Jaber AlwedyanAbstract: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
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Detecting Phishing Websites Using Associative Classification
European Journal of Business and Management, 2013Co-Authors: Moh'd Iqbal Al Ajlouni, Wael Hadi, Jaber AlwedyanAbstract: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
Moh'd Iqbal Al Ajlouni - One of the best experts on this subject based on the ideXlab platform.
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Detecting Phishing Websites Using Associative Classification
European Journal of Business and Management, 2013Co-Authors: Moh'd Iqbal Al Ajlouni, Wael Hadi, Jaber AlwedyanAbstract: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
Al Ajlouni - One of the best experts on this subject based on the ideXlab platform.
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Detecting Phishing Websites Using Associative Classification
2016Co-Authors: Al Ajlouni, Jaber AlwedyanAbstract: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
Jose Nazario - One of the best experts on this subject based on the ideXlab platform.
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MALWARE - Phishing by form: The abuse of form sites
2011 6th International Conference on Malicious and Unwanted Software, 2011Co-Authors: Hugo Gonzalez, Kara Nance, Jose NazarioAbstract:The evolution of phishing methods has resulted in a plethora of new tools and techniques to coerce users into providing Credentials, generally for nefarious purposes. This paper discusses the relatively recent emergence of an evolutionary phishing technique called phishing by form that relies on the abuse of online forms to elicit information from the target population. We evaluate a phishing corpus of emails and over a year's worth of phishing URLs to investigate the methodology, history, spread, origins, and life cycle as well as identifying directions for future research in this area. Our analysis finds that these hosted sites represent less than 1% of all phishing URLs, appear to have shorter active lifetimes, and focus mainly on email Account Credential theft. We also provide defensive recommendations for these free application sites and users.
Wael Hadi - One of the best experts on this subject based on the ideXlab platform.
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Detecting Phishing Websites Using Associative Classification
European Journal of Business and Management, 2013Co-Authors: Moh'd Iqbal Al Ajlouni, Wael Hadi, Jaber AlwedyanAbstract: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
