When traditional machine learning methods are applied to network intrusion detection, they need to rely on expert knowledge to extract feature vectors in advance, which incurs lack of flexibility and versatility. Recently, deep learning methods have shown superior performance compared with traditional machine learning methods. Deep learning methods can learn the raw data directly, but they are faced with expensive computing cost. To solve this problem, a preprocessing method based on multipacket input unit and compression is proposed, which takes m data packets as the input unit to maximize the retention of information and greatly compresses the raw traffic to shorten the data learning and training time. In our proposed method, the CNN network structure is optimized and the weights of some convolution layers are assigned directly by using the Gabor filter. Experimental results on the benchmark data set show that compared with the existing models, the proposed method improves the detection accuracy by 2.49% and reduces the training time by 62.1%. In addition, the experiments show that the proposed compression method has obvious advantages in detection accuracy and computational efficiency compared with the existing compression methods.

中文翻译：

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FCNN：一种基于原始网络流量的高效入侵检测方法

传统的机器学习方法应用于网络入侵检测时，需要依赖专家知识提前提取特征向量，缺乏灵活性和通用性。最近，与传统机器学习方法相比，深度学习方法表现出优越的性能。深度学习方法可以直接学习原始数据，但面临着昂贵的计算成本。针对这一问题，提出了一种基于多包输入单元和压缩的预处理方法，其取m以数据包为输入单元，最大限度地保留信息，大大压缩原始流量，缩短数据学习和训练时间。在我们提出的方法中，优化了 CNN 网络结构，并使用 Gabor 滤波器直接分配了一些卷积层的权重。在基准数据集上的实验结果表明，与现有模型相比，所提方法的检测精度提高了 2.49%，训练时间减少了 62.1%。此外，实验表明，与现有的压缩方法相比，所提出的压缩方法在检测精度和计算效率方面具有明显的优势。