论文标题
基于深度信念网络的入侵检测系统
An Intrusion Detection System based on Deep Belief Networks
论文作者
论文摘要
连接设备的快速增长导致了新型网络安全威胁的扩散，称为零日攻击。传统的基于行为的ID依靠DNN来检测这些攻击。用于训练DNN的数据集的质量在检测性能中起着至关重要的作用，其代表性不足导致性能不佳。在本文中，我们开发和评估了DBN在连接设备网络中检测网络攻击方面的性能。 CICIDS2017数据集用于训练和评估我们提出的DBN方法的性能。应用和评估了几种类平衡技术。最后，我们将我们的方法与常规的MLP模型和现有的最新方法进行了比较。我们提出的DBN方法显示出竞争性和有希望的结果，并且在培训数据集中攻击不足的攻击方面的检测显着提高。
The rapid growth of connected devices has led to the proliferation of novel cyber-security threats known as zero-day attacks. Traditional behaviour-based IDS rely on DNN to detect these attacks. The quality of the dataset used to train the DNN plays a critical role in the detection performance, with underrepresented samples causing poor performances. In this paper, we develop and evaluate the performance of DBN on detecting cyber-attacks within a network of connected devices. The CICIDS2017 dataset was used to train and evaluate the performance of our proposed DBN approach. Several class balancing techniques were applied and evaluated. Lastly, we compare our approach against a conventional MLP model and the existing state-of-the-art. Our proposed DBN approach shows competitive and promising results, with significant performance improvement on the detection of attacks underrepresented in the training dataset.
