论文标题
通过改进的双向长期短期记忆神经网络检测恶意要求检测
Malicious Requests Detection with Improved Bidirectional Long Short-term Memory Neural Networks
论文作者
论文摘要
检测和拦截恶意请求是反对网络安全攻击的最广泛使用的方法之一。大多数现有的检测方法，包括匹配的黑名单字符和机器学习算法，都显示出容易受到复杂攻击的影响。为了解决上述问题，需要一种更一般和更严格的检测方法。在本文中，我们提出了将恶意请求作为时间序列分类问题的问题，并提出了一种新型的深度学习模型，即卷积神经网络 - 指示长的短期记忆 - 记忆 - 扭转神经网络（CNN-BILSTM-CNN）。通过连接卷积层的阴影和深度特征地图，恶意提取能力在更详细的功能上得到了提高。 HTTP数据集CSIC 2010上的实验结果证明了与最新技术相比，该方法的有效性。
Detecting and intercepting malicious requests are one of the most widely used ways against attacks in the network security. Most existing detecting approaches, including matching blacklist characters and machine learning algorithms have all shown to be vulnerable to sophisticated attacks. To address the above issues, a more general and rigorous detection method is required. In this paper, we formulate the problem of detecting malicious requests as a temporal sequence classification problem, and propose a novel deep learning model namely Convolutional Neural Network-Bidirectional Long Short-term Memory-Convolutional Neural Network (CNN-BiLSTM-CNN). By connecting the shadow and deep feature maps of the convolutional layers, the malicious feature extracting ability is improved on more detailed functionality. Experimental results on HTTP dataset CSIC 2010 have demonstrated the effectiveness of the proposed method when compared with the state-of-the-arts.
