Cinar Ahmet Cevahir, Kara Turkan Beyza
Department of Computer Engineering, Faculty of Technology, Selçuk University, Konya, Turkey.
Multimed Tools Appl. 2023;82(13):20269-20281. doi: 10.1007/s11042-023-14400-6. Epub 2023 Jan 30.
Smartphones have become small computers that meet many of our needs, from e-mail and banking transactions to communication and social media use. In line with these attractive functions, the use of smartphones has greatly increased over the years. One of the most important features of these mobile devices is that they offer users many mobile applications that they can install. However, hacker attacks and the spread of malware have also increased. Today, current mobile malware detection and defense technologies are still inadequate. Mobile security is not only directly related to the operating system and used device but also related with communication over the internet, data encryption, data summarization, and users' privacy awareness. The main aim and contribution of this study are to collect the current state of mobile security and highlight the future of mobile security in light of the recent mobile security threat reports. The studies in the field of malware, attack types, and security vulnerabilities concerning the usage of smartphones were analyzed. The malware detection techniques were analyzed into two categories: signature-based and machine learning (behavior detection)-based techniques. Additionally, the current threats and prevention methods were described. Finally, a future direction is highlighted in the light of the current mobile security reports.
智能手机已成为满足我们诸多需求的小型计算机，从电子邮件、银行交易到通信和社交媒体使用。随着这些吸引人的功能，智能手机的使用在过去几年中大幅增加。这些移动设备最重要的特性之一是它们为用户提供了许多可安装的移动应用程序。然而，黑客攻击和恶意软件的传播也有所增加。如今，当前的移动恶意软件检测和防御技术仍然不足。移动安全不仅与操作系统和使用的设备直接相关，还与通过互联网的通信、数据加密、数据汇总以及用户的隐私意识有关。本研究的主要目的和贡献在于收集移动安全的当前状况，并根据近期的移动安全威胁报告突出移动安全的未来发展。分析了智能手机使用方面恶意软件、攻击类型和安全漏洞领域的研究。恶意软件检测技术分为两类：基于特征码的技术和基于机器学习（行为检测）的技术。此外，还描述了当前的威胁和预防方法。最后，根据当前的移动安全报告突出了未来的发展方向。
