The techniques used in modern attacks have become an important factor for investigation. As we advance further into the digital age, cyber attackers are employing increasingly sophisticated and highly threatening methods. These attacks target not only organizations and governments but also extend to private and corporate sectors. Modern attack techniques, such as lateral movement and ransomware, are designed to infiltrate networks and steal sensitive data. Among these techniques, Advanced Persistent Threats (APTs) represent a complex method of attack aimed at specific targets to steal high-value sensitive information or damage the infrastructure of the targeted organization. In this paper, I will investigate Advanced Persistent Threats (APTs) as a modern attack technique, focusing on both the attack life cycle and cutting-edge detection and defense strategies proposed in recent academic research. I will analyze four representative papers to understand the evolution of APT detection mechanisms, including machine learning-driven behavioral analysis and network-level collaborative defense models. Through this comparative analysis, I aim to highlight the strengths and limitations of each approach and propose more adaptive APT mitigation strategies. The study seeks to analyze the key characteristics of APTs and provide a comprehensive high-level understanding of APTs along with potential solutions to the threats they pose.

翻译：现代攻击技术已成为调查分析的重要因素。随着数字时代的深入发展，网络攻击者正采用日益复杂且极具威胁性的手段。这些攻击不仅针对政府机构，更延伸至私营部门与企业领域。诸如横向移动和勒索软件等现代攻击技术，旨在渗透网络并窃取敏感数据。在这些技术中，高级持续性威胁（APTs）代表了一种针对特定目标实施的复杂攻击方法，其目的在于窃取高价值敏感信息或破坏目标组织的基础设施。本文将以高级持续性威胁（APTs）作为现代攻击技术的研究对象，聚焦于攻击生命周期及近期学术研究提出的前沿检测与防御策略。通过分析四篇代表性论文，本研究将探讨APT检测机制的演进路径，包括基于机器学习的行为分析与网络级协同防御模型。通过比较分析，本文旨在阐明各类方法的优势与局限，并提出更具适应性的APT缓解策略。本研究试图解析APTs的核心特征，提供对APTs的全面高层认知，并针对其构成的威胁提出潜在解决方案。