Different types of connectivity are available on smartphones such as WiFi, infrared, Bluetooth, GPRS, GPS, and GSM. The ubiquitous computing features of smartphones make them a vital part of our lives. The boom in smartphone technology has unfortunately attracted hackers and crackers as well. Smartphones have become the ideal hub for malware, gray ware, and spyware writers to exploit smartphone vulnerabilities and insecure communication channels. For every security service introduced, there is simultaneously a counterattack to breach the security and vice versa. Until a new mechanism is discovered, the diverse classifications of technology mean that one security contrivance cannot be a remedy for phishing attacks in all circumstances. Therefore, a novel architecture for antiphishing is mandatory that can compensate web page protection and authentication from falsified web pages on smartphones. In this paper, we developed a cluster-based antiphishing (CAP) model, which is a lightweight scheme specifically for smartphones to save energy in portable devices. The model is significant in identifying, clustering, and preventing phishing attacks on smartphone platforms. Our CAP model detects and prevents illegal access to smartphones based on clustering data to legitimate/normal and illegitimate/abnormal. First, we evaluated our scheme with mathematical and algorithmic methods. Next, we conducted a real test bed to identify and counter phishing attacks on smartphones which provided 90% accuracy in the detection system as true positives and less than 9% of the results as true negative.

中文翻译：

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用于智能手机的基于集群的反钓鱼 (CAP) 模型

智能手机提供不同类型的连接，例如 WiFi、红外线、蓝牙、GPRS、GPS 和 GSM。智能手机无处不在的计算功能使它们成为我们生活的重要组成部分。不幸的是，智能手机技术的繁荣也吸引了黑客和破解者。智能手机已成为恶意软件、灰色软件和间谍软件编写者利用智能手机漏洞和不安全通信渠道的理想中心。对于引入的每项安全服务，都会同时进行反击以破坏安全，反之亦然。在发现一种新机制之前，技术的不同分类意味着一种安全设计不能在所有情况下都成为网络钓鱼攻击的补救措施。所以，一种新型的反网络钓鱼架构是强制性的，它可以补偿智能手机上伪造网页的网页保护和身份验证。在本文中，我们开发了一种基于集群的反网络钓鱼 (CAP) 模型，这是一种轻量级方案，专门用于智能手机以节省便携式设备的能源。该模型在识别、聚类和防止对智能手机平台的网络钓鱼攻击方面具有重要意义。我们的 CAP 模型基于将数据聚类为合法/正常和非法/异常来检测和防止对智能手机的非法访问。首先，我们用数学和算法方法评估了我们的方案。接下来，我们进行了一个真实的测试平台来识别和反击对智能手机的网络钓鱼攻击，它在检测系统中提供了 90% 的准确率作为真阳性，而不到 9% 的结果为真阴性。