A Lightweight Anti-Phishing Technique for Mobile Phone

IF 0.8 Q4 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Acta Informatica Pragensia Pub Date : 2017-12-31 DOI:10.18267/j.aip.104
A. A. Orunsolu, Misturah Adunni Alaran, Adeleke Amos Adebayo, S. O. Kareem, Ayobami Oke
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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.
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一种轻量级的手机反网络钓鱼技术
手机已经成为一种必不可少的上网设备。这是由于便携性、低成本和易用性的优势。然而,使用手机进行在线活动现在正受到无数网络犯罪的挑战。其中一种犯罪是网络钓鱼攻击。在这项工作中,提出了一种轻量级的反网络钓鱼技术来对抗移动设备上的网络钓鱼攻击。这是必要的,因为这些移动平台增加了钓鱼者的攻击面,同时降低了现有对策的有效性。该方法基于从PhishTank中提取的网络钓鱼特征的频率分析,使用许多URL行为来确定网站的状态。为了提高未知模式的检测能力,采用了支持向量机的机器学习算法。结果表明,该方法对网络钓鱼网站的检测非常有效,假阴性可以忽略不计。
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来源期刊
Acta Informatica Pragensia
Acta Informatica Pragensia Social Sciences-Library and Information Sciences
CiteScore
1.70
自引率
0.00%
发文量
26
审稿时长
12 weeks
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