A. A. Orunsolu, Misturah Adunni Alaran, Adeleke Amos Adebayo, S. O. Kareem, Ayobami Oke
{"title":"A Lightweight Anti-Phishing Technique for Mobile Phone","authors":"A. A. Orunsolu, Misturah Adunni Alaran, Adeleke Amos Adebayo, S. O. Kareem, Ayobami Oke","doi":"10.18267/j.aip.104","DOIUrl":null,"url":null,"abstract":"Mobile phones have become an essential device for accessing the web. This is due to the advantages of portability, lower cost and ease. However, the adoption of mobile phones for online activities is now being challenged by myriads of cybercrimes. One of such crimes is phishing attack. In this work, a lightweight anti-phishing technique is proposed to combat phishing attacks on mobile devices. This is necessary because these mobile platforms have increased the attack surface for phishers while diminishing the effectiveness of existing countermeasures. The proposed approach uses a number of URL behavior to determine the status of a website based on frequency analysis of extracted phishing features from PhishTank. To increase the detection power of unknown pattern, a machine learning algorithm called Support Vector Machine is adopted. The results indicated that the approach is very efficient against phishing sites with negligible false negatives.","PeriodicalId":36592,"journal":{"name":"Acta Informatica Pragensia","volume":"6 1","pages":"114-123"},"PeriodicalIF":0.8000,"publicationDate":"2017-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Acta Informatica Pragensia","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.18267/j.aip.104","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 3
Abstract
Mobile phones have become an essential device for accessing the web. This is due to the advantages of portability, lower cost and ease. However, the adoption of mobile phones for online activities is now being challenged by myriads of cybercrimes. One of such crimes is phishing attack. In this work, a lightweight anti-phishing technique is proposed to combat phishing attacks on mobile devices. This is necessary because these mobile platforms have increased the attack surface for phishers while diminishing the effectiveness of existing countermeasures. The proposed approach uses a number of URL behavior to determine the status of a website based on frequency analysis of extracted phishing features from PhishTank. To increase the detection power of unknown pattern, a machine learning algorithm called Support Vector Machine is adopted. The results indicated that the approach is very efficient against phishing sites with negligible false negatives.