The Experts below are selected from a list of 177 Experts worldwide ranked by ideXlab platform
M A Hossain - One of the best experts on this subject based on the ideXlab platform.
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SKIMA - Deep Learning with Convolutional Neural Network and Long Short-Term Memory for Phishing Detection
2019 13th International Conference on Software Knowledge Information Management and Applications (SKIMA), 2019Co-Authors: Moruf A. Adebowale, Khin T. Lwin, M A HossainAbstract:Phishers sometimes exploit users’ trust of a known webSite’s appearance by using a similar page that looks like the Legitimate Site. In recent times, researchers have tried to identify and classify the issues that can contribute to the detection of phishing webSites. This study focuses on design and development of a deep learning based phishing detection solution that leverages the Universal Resource Locator and webSite content such as images and frame elements. A Convolutional Neural Network (CNN) and the Long Short-Term Memory (LSTM) algorithm were used to build a classification model. The experimental results showed that the proposed model achieved an accuracy rate of 93.28%.
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Deep Learning with Convolutional Neural Network and Long Short-Term Memory for Phishing Detection
2019 13th International Conference on Software Knowledge Information Management and Applications (SKIMA), 2019Co-Authors: Moruf A. Adebowale, Khin T. Lwin, M A HossainAbstract:Phishers sometimes exploit users' trust of a known webSite's appearance by using a similar page that looks like the Legitimate Site. In recent times, researchers have tried to identify and classify the issues that can contribute to the detection of phishing webSites. This study focuses on design and development of a deep learning based phishing detection solution that leverages the Universal Resource Locator and webSite content such as images and frame elements. A Convolutional Neural Network (CNN) and the Long Short-Term Memory (LSTM) algorithm were used to build a classification model. The experimental results showed that the proposed model achieved an accuracy rate of 93.28%.
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intelligent phishing detection and protection scheme for online transactions
Expert Systems With Applications, 2013Co-Authors: Phoebe Barraclough, Muhammad Atif Tahir, Graham Sexton, M A Hossain, Nauman AslamAbstract:Phishing is an instance of social engineering techniques used to deceive users into giving their sensitive information using an ilLegitimate webSite that looks and feels exactly like the target organization webSite. Most phishing detection approaches utilizes Uniform Resource Locator (URL) blacklists or phishing webSite features combined with machine learning techniques to combat phishing. Despite the existing approaches that utilize URL blacklists, they cannot generalize well with new phishing attacks due to human weakness in verifying blacklists, while the existing feature-based methods suffer high false positive rates and insufficient phishing features. As a result, this leads to an inadequacy in the online transactions. To solve this problem robustly, the proposed study introduces new inputs (Legitimate Site rules, User-behavior profile, PhishTank, User-specific Sites, Pop-Ups from emails) which were not considered previously in a single protection platform. The idea is to utilize a Neuro-Fuzzy Scheme with 5 inputs to detect phishing Sites with high accuracy in real-time. In this study, 2-Fold cross-validation is applied for training and testing the proposed model. A total of 288 features with 5 inputs were used and has so far achieved the best performance as compared to all previously reported results in the field.
Arturo Ribagorda - One of the best experts on this subject based on the ideXlab platform.
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a strong authentication protocol based on portable one time dynamic urls
Web Intelligence, 2010Co-Authors: E Galan, Julio C Hernandezcastro, Almudena Alcaide, Arturo RibagordaAbstract:This work proposes a new strong authentication protocol for the prevention of identity and private personal data theft suffered by users in the Internet. Identity theft is a problem of rising impact amongst Internet users and service providers and it occurs, very frequently, through techniques like phishing. The main reason for the high rates of success is user unexperience and their inability to pay attention to the details that allow them to tell a Legitimate Site from its fake version. In this paper we present a new strong 3–phaseauthentication protocol which makes use of Portable One–TimeDynamic URLs for the prevention of identity theft over the Internet. Moreover, a prototype of such a scheme has been implemented to measure the usability and scalability of the proposal.
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Web Intelligence - A Strong Authentication Protocol Based on Portable One-Time Dynamic URLs
2010 IEEE WIC ACM International Conference on Web Intelligence and Intelligent Agent Technology, 2010Co-Authors: E Galan, Almudena Alcaide, Julio C. Hernandez-castro, Arturo RibagordaAbstract:This work proposes a new strong authentication protocol for the prevention of identity and private personal data theft suffered by users in the Internet. Identity theft is a problem of rising impact amongst Internet users and service providers and it occurs, very frequently, through techniques like phishing. The main reason for the high rates of success is user unexperience and their inability to pay attention to the details that allow them to tell a Legitimate Site from its fake version. In this paper we present a new strong 3–phaseauthentication protocol which makes use of Portable One–TimeDynamic URLs for the prevention of identity theft over the Internet. Moreover, a prototype of such a scheme has been implemented to measure the usability and scalability of the proposal.
Dewa Gede Hendra Divayana - One of the best experts on this subject based on the ideXlab platform.
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Publication of the society’s sensitive personal data in the Legitimate Site of the general election committee
Journal of Physics: Conference Series, 2020Co-Authors: A Parwata, Kadek Yota Ernanda Aryanto, Dewa Gede Hendra DivayanaAbstract:It is highly important to protect the society's sensitive personal data. The reason is that many personal data are published by the government institution without paying attention to the prevailing regulations. In this current study, the search for the data in the form of hyperlink with its contents in the Legitimate Site of the General Election Committees was conducted with the assistance of Crawling Web. The obtained contents were preprocessed using the Preprocessing Text, which were then weighted using the TF-IDF method before they were classified using the Naїve Bayes method. After that, an analysis on the types of the published sensitive personal and the extent of the publication based on the area groups was conducted. Out of 6,700 instances of the personal data which were analyzed, 6.430 were published. The personal data which were published were full name, place and date of birth, religion, marital status, ID Number of the government civil servants, identity card number, number of the tax payer, account number, mobile number, e-mail, address, position, and face photo. The level of publication based on the total data found was as follows: 11.45% in the Central General Election Committee, 21.60% in the eastern area, 17.01% in the central area and 49.94% in the eastern area. The accuracy of the Naїve Bayes method averaged 96.99%. Prior to publication, the General Election Committee is recommended to respect someone's personal data as privacy and the data which would be published should obtain approval and an easily-contacted contact person.
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publication of the society s sensitive personal data in the Legitimate Site of the general election committee
Journal of Physics: Conference Series, 2020Co-Authors: A Parwata, Kadek Yota Ernanda Aryanto, Dewa Gede Hendra DivayanaAbstract:It is highly important to protect the society's sensitive personal data. The reason is that many personal data are published by the government institution without paying attention to the prevailing regulations. In this current study, the search for the data in the form of hyperlink with its contents in the Legitimate Site of the General Election Committees was conducted with the assistance of Crawling Web. The obtained contents were preprocessed using the Preprocessing Text, which were then weighted using the TF-IDF method before they were classified using the Naїve Bayes method. After that, an analysis on the types of the published sensitive personal and the extent of the publication based on the area groups was conducted. Out of 6,700 instances of the personal data which were analyzed, 6.430 were published. The personal data which were published were full name, place and date of birth, religion, marital status, ID Number of the government civil servants, identity card number, number of the tax payer, account number, mobile number, e-mail, address, position, and face photo. The level of publication based on the total data found was as follows: 11.45% in the Central General Election Committee, 21.60% in the eastern area, 17.01% in the central area and 49.94% in the eastern area. The accuracy of the Naїve Bayes method averaged 96.99%. Prior to publication, the General Election Committee is recommended to respect someone's personal data as privacy and the data which would be published should obtain approval and an easily-contacted contact person.
Nauman Aslam - One of the best experts on this subject based on the ideXlab platform.
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intelligent phishing detection and protection scheme for online transactions
Expert Systems With Applications, 2013Co-Authors: Phoebe Barraclough, Muhammad Atif Tahir, Graham Sexton, M A Hossain, Nauman AslamAbstract:Phishing is an instance of social engineering techniques used to deceive users into giving their sensitive information using an ilLegitimate webSite that looks and feels exactly like the target organization webSite. Most phishing detection approaches utilizes Uniform Resource Locator (URL) blacklists or phishing webSite features combined with machine learning techniques to combat phishing. Despite the existing approaches that utilize URL blacklists, they cannot generalize well with new phishing attacks due to human weakness in verifying blacklists, while the existing feature-based methods suffer high false positive rates and insufficient phishing features. As a result, this leads to an inadequacy in the online transactions. To solve this problem robustly, the proposed study introduces new inputs (Legitimate Site rules, User-behavior profile, PhishTank, User-specific Sites, Pop-Ups from emails) which were not considered previously in a single protection platform. The idea is to utilize a Neuro-Fuzzy Scheme with 5 inputs to detect phishing Sites with high accuracy in real-time. In this study, 2-Fold cross-validation is applied for training and testing the proposed model. A total of 288 features with 5 inputs were used and has so far achieved the best performance as compared to all previously reported results in the field.
K S Kuppusamy - One of the best experts on this subject based on the ideXlab platform.
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phidma a phishing detection model with multi filter approach
Journal of King Saud University - Computer and Information Sciences, 2017Co-Authors: Gunikhan Sonowal, K S KuppusamyAbstract:Abstract Phishing remains a basic security issue in the cyberspace. In phishing, assailants steal sensitive information from victims by providing a fake Site which looks like the visual clone of a Legitimate Site. Phishing shall be handled using various approaches. It is established that single filter methods would be insufficient to detect different categories of phishing attempts. This paper provides a multilayer model to detect phishing, titled as PhiDMA(Phishing Detection using Multi-filter Approach). The PhiDMA model incorporates five layers: Auto upgrade whitelist layer, URL features layer, Lexical signature layer, String matching layer and Accessibility Score comparison layer. A prototype implementation of the proposed PhiDMA model is built with an accessible interface so that persons with visual impairments shall access it without any barrier. The result from the experiment shows that the model is capable to detect phishing Sites with an accuracy of 92.72%.
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PhiDMA – A phishing detection model with multi-filter approach
Journal of King Saud University - Computer and Information Sciences, 2017Co-Authors: Gunikhan Sonowal, K S KuppusamyAbstract:Abstract Phishing remains a basic security issue in the cyberspace. In phishing, assailants steal sensitive information from victims by providing a fake Site which looks like the visual clone of a Legitimate Site. Phishing shall be handled using various approaches. It is established that single filter methods would be insufficient to detect different categories of phishing attempts. This paper provides a multilayer model to detect phishing, titled as PhiDMA(Phishing Detection using Multi-filter Approach). The PhiDMA model incorporates five layers: Auto upgrade whitelist layer, URL features layer, Lexical signature layer, String matching layer and Accessibility Score comparison layer. A prototype implementation of the proposed PhiDMA model is built with an accessible interface so that persons with visual impairments shall access it without any barrier. The result from the experiment shows that the model is capable to detect phishing Sites with an accuracy of 92.72%.