面向僵尸网络DGA攻击的智能检测技术与对抗策略研究

The Research on Intelligent Detection Technology and Countermeasures for Botnet DGA Attacks

摘要

僵尸网络通过域名生成算法（Domain Generation Algorithms,DGA）能够动态生成大量难以预测的域名，从而规避传统静态监测机制，提升恶意活动的隐蔽性与持久性。随着DGA技术的不断演进，传统检测方法面临的挑战愈加严峻。如何高效识别与防范此类域名成为网络安全领域的关键问题。本文系统分析当前主流的DGA检测技术，涵盖基于统计特征、机器学习及深度学习的方法，深入探讨其工作原理、适用场景与性能表现，揭示现有研究在误报率、计算复杂度、数据集规模及新型DGA适应性等方面的不足。最后，本文提出深度学习优化与跨域协同检测的创新方向，并结合流量行为分析与生成规律阻断机制，构建多层次、综合性的DGA防御体系，为提升检测技术的有效性、准确性与适应性提供新思路。

Abstract

Botnets can dynamically generate numerous unpredictable domains via Domain Generation Algorithms (DGA) to elude traditional static detection, enhancing the stealth and persistence of malicious activities. As DGA technology advances, traditional detection methods are facing growing challenges. Efficiently identifying and defending against these domains has become crucial in cybersecurity. This paper comprehensively analyzes mainstream DGA detection technologies, including those based on statistical features, machine learning, and deep learning. It delves into their principles, application scenarios, and performance, uncovering limitations in false positive rates, computational complexity, dataset size, and adaptability to new DGAs. Finally, the paper proposes innovative directions for deep learning-based detection and cross domain collaborative detection. Combined with traffic behavior analysis and generation-pattern blocking mechanisms, we build a multi-Layered, integrated DGA defense system, offering new ideas to improve detection effectiveness, accuracy, and adaptability.

Graphical abstract

关键词

僵尸网络 / 域名生成算法 / 域名检测 / 机器学习

Key words

botnet / domain generation algorithm / domain detection / machine learning

基金资助

国家自然科学基金(61872226)