基于深度学习的恶意域名检测与防御系统研究 毕业论文+任务书+参考文献+论文检测查重报告+源码及数据
摘 要
本文综述了DGA（域名生成算法）域名在网络安全领域的挑战及其传统检测方法，指出了这些方法在实时性、准确率和效率方面的不足。随着网络攻击手段的不断进化，DGA域名已成为攻击者逃避安全检测的重要手段。因此，开发高效、实时的DGA域名检测方法至关重要。
近年来，深度学习在自然语言处理领域取得了显著进展，为DGA域名检测提供了新的解决方案。本文详细介绍了利用LSTM模型和Attention机制进行DGA域名检测的方法。通过将域名字符串转换为词向量，LSTM模型能够捕获序列中的长期依赖关系，而Attention机制则有助于模型更准确地关注关键信息。
实验结果表明，基于深度学习的DGA域名检测方法在准确率、实时性和效率方面均优于传统方法。该方法不仅能够有效识别DGA域名，还能降低误报率和漏报率，为网络安全提供了有力保障。
本文的贡献在于为DGA域名检测提供了新的视角和思路，推动了相关技术的发展和进步。未来，我们将继续探索更多有效的DGA域名检测方法，以应对不断变化的网络安全挑战，保护用户数据安全。
关键词：恶意域名；域名检测；Python；DGA；LSTM模型
Abstract
This paper reviews the challenges of DGA (domain name generation algorithm) domain name and its traditional detection methods, pointing out the shortcomings of these methods in terms of real-time performance, accuracy and efficiency. With the continuous evolution of network attack means, DGA domain name has become an important means for attackers to escape security detection.
Therefore, it is crucial to develop efficient and real-time DGA domain name detection methods. In recent years, deep learning has made remarkable progress in the field of natural language processing, providing new solutions for DGA domain name detection. This paper details the method of DGA domain detection using LSTM model and Attention mechanism. By converting domain name strings into word vectors, the LSTM model is able to capture long-term dependencies in the sequence, while the Attention mechanism helps the model to focus on critical information more accurately.
The experimental results show that deep learning-based DGA domain name detection methods outperform conventional methods in terms of accuracy, real-time performance, and efficiency. This method can not only effectively identify the DGA domain name, but also reduce the false alarm rate and omission rate, which provides a strong guarantee for network security.
The contribution of this paper is to provide a new perspective and thinking for DGA domain name detection, and promote the development and progress of related technologies. In the future, we will continue to explore more effective DGA domain name detection methods to meet the changing network security challenges and protect user data security.
Key words: malicious domain name; domain name detection; Python; DGA; LSTM model
目 录
