用图像化方法压缩网络流量，1秒内高效发现异常。
Unsupervised Network Anomaly Detection with Autoencoders and Traffic Images
- 将网络流量转为图像，实现1秒窗口的紧凑表示。
- 无需标注数据，直接识别异常流量模式。
- 适合资源受限设备部署，兼顾效率与准确率。
随着联网设备数量激增，快速检测安全问题的需求日益迫切。海量通信流带来巨量数据处理压力，且设备异构性明显，计算能力差异大。为此，本文提出一种基于图像的网络流量表示方法，能在1秒时间窗内生成当前网络状态的紧凑摘要，有效凸显异常特征，降低复杂处理架构需求。同时设计了一种无监督学习方法，可精准检测异常。代码与数据集已公开于 https://github.com/michaelneri/image-based-network-traffic-anomaly-detection。
原文摘要 · Abstract (English)
Due to the recent increase in the number of connected devices, the need to promptly detect security issues is emerging. Moreover, the high number of communication flows creates the necessity of processing huge amounts of data. Furthermore, the connected devices are heterogeneous in nature, having different computational capacities. For this reason, in this work we propose an image-based representation of network traffic which allows to realize a compact summary of the current network conditions with 1-second time windows. The proposed representation highlights the presence of anomalies thus reducing the need for complex processing architectures. Finally, we present an unsupervised learning approach which effectively detects the presence of anomalies. The code and the dataset are available at https://github.com/michaelneri/image-based-network-traffic-anomaly-detection.
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