Biblio

Filters: Author is Amsaad, Fathi  [Clear All Filters]
2021-05-20
Razaque, Abdul, Frej, Mohamed Ben Haj, Sabyrov, Dauren, Shaikhyn, Aidana, Amsaad, Fathi, Oun, Ahmed.  2020.  Detection of Phishing Websites using Machine Learning. 2020 IEEE Cloud Summit. :103—107.

Phishing sends malicious links or attachments through emails that can perform various functions, including capturing the victim's login credentials or account information. These emails harm the victims, cause money loss, and identity theft. In this paper, we contribute to solving the phishing problem by developing an extension for the Google Chrome web browser. In the development of this feature, we used JavaScript PL. To be able to identify and prevent the fishing attack, a combination of Blacklisting and semantic analysis methods was used. Furthermore, a database for phishing sites is generated, and the text, links, images, and other data on-site are analyzed for pattern recognition. Finally, our proposed solution was tested and compared to existing approaches. The results validate that our proposed method is capable of handling the phishing issue substantially.

2020-03-02
Gulsezim, Duisen, Zhansaya, Seiitkaliyeva, Razaque, Abdul, Ramina, Yestayeva, Amsaad, Fathi, Almiani, Muder, Ganda, Raouf, Oun, Ahmed.  2019.  Two Factor Authentication using Twofish Encryption and Visual Cryptography Algorithms for Secure Data Communication. 2019 Sixth International Conference on Internet of Things: Systems, Management and Security (IOTSMS). :405–411.
Dependence of the individuals on the Internet for performing the several actions require secure data communication. Thus, the reliable data communication improves the confidentiality. As, enhanced security leads to reliable and faster communication. To improve the reliability and confidentiality, there is dire need of fully secured authentication method. There are several methods of password protections were introduced to protect the confidentiality and reliability. Most of the existing methods are based on alphanumeric approaches, but few methods provide the dual authentication process. In this paper, we introduce improved graphical password authentication using Twofish Encryption and Visual Cryptography (TEVC) method. Our proposed TEVC is unpredictably organized as predicting the correct graphical password and arranging its particles in the proper order is harder as compared to traditional alphanumeric password system. TEVC is tested by using JAVA platform. Based on the testing results, we confirm that proposed TEVC provides secure authentication. TEVC encryption algorithm detected as more prudent and possessing lower time complexity as compared to other known existing algorithms message code confirmation and fingerprint scan with password.