Data protection is the process of securing sensitive information from being corrupted, compromised, or lost. A hyperconnected network, on the other hand, is a computer networking trend in which communication occurs over a network. However, what about malware. Malware is malicious software meant to penetrate private data, threaten a computer system, or gain unauthorised network access without the users consent. Due to the increasing applications of computers and dependency on electronically saved private data, malware attacks on sensitive information have become a dangerous issue for individuals and organizations across the world. Hence, malware defense is critical for keeping our computer systems and data protected. Many recent survey articles have focused on either malware detection systems or single attacking strategies variously. To the best of our knowledge, no survey paper demonstrates malware attack patterns and defense strategies combinedly. Through this survey, this paper aims to address this issue by merging diverse malicious attack patterns and machine learning (ML) based detection models for modern and sophisticated malware. In doing so, we focus on the taxonomy of malware attack patterns based on four fundamental dimensions the primary goal of the attack, method of attack, targeted exposure and execution process, and types of malware that perform each attack. Detailed information on malware analysis approaches is also investigated. In addition, existing malware detection techniques employing feature extraction and ML algorithms are discussed extensively. Finally, it discusses research difficulties and unsolved problems, including future research directions.

翻译：数据保护是指保护敏感信息免受破坏、泄露或丢失的过程。超连接网络是一种计算机联网趋势，通信通过网络进行。然而，恶意软件问题不容忽视。恶意软件是一种恶意程序，旨在未经用户同意侵入私人数据、威胁计算机系统或获取未授权网络访问。随着计算机应用的日益普及以及对电子存储私人数据的依赖，针对敏感信息的恶意软件攻击已成为全球个人和组织面临的危险问题。因此，恶意软件防御对于保护计算机系统和数据安全至关重要。近期许多综述文章聚焦于恶意软件检测系统或单一攻击策略，但据我们所知，尚无综述论文综合展示恶意软件攻击模式与防御策略。本综述旨在通过融合多样化恶意攻击模式与基于机器学习（ML）的检测模型，解决现代复杂恶意软件问题。为此，我们基于四个基本维度（攻击的主要目标、攻击方法、目标暴露与执行过程，以及执行每种攻击的恶意软件类型）构建了恶意软件攻击模式的分类体系。还深入研究了恶意软件分析方法。此外，广泛讨论了现有采用特征提取和机器学习算法的恶意软件检测技术。最后，探讨了研究难点与未解决问题，包括未来研究方向。