论文标题
使用协议模糊生成全面数据，以应用深度学习来检测网络攻击
Generating Comprehensive Data with Protocol Fuzzing for Applying Deep Learning to Detect Network Attacks
论文作者
论文摘要
网络攻击已成为全球组织的主要安全问题，并引起了学者的关注。最近，研究人员应用了神经网络来检测使用网络日志的网络攻击。但是，公共网络数据集具有主要缺点，例如有限的数据样本变化以及关于恶意和良性样本的不平衡数据。在本文中，我们提出了一种新方法，即协议模糊，以自动生成高质量的网络数据，可以在其中培训深度学习模型。我们的发现表明，模糊生成涵盖现实世界数据的数据样本和经过模糊数据训练的深度学习模型可以成功地检测真正的网络攻击。
Network attacks have become a major security concern for organizations worldwide and have also drawn attention in the academics. Recently, researchers have applied neural networks to detect network attacks with network logs. However, public network data sets have major drawbacks such as limited data sample variations and unbalanced data with respect to malicious and benign samples. In this paper, we present a new approach, protocol fuzzing, to automatically generate high-quality network data, on which deep learning models can be trained. Our findings show that fuzzing generates data samples that cover real-world data and deep learning models trained with fuzzed data can successfully detect real network attacks.
