FastPacket：迈向基于FastText的预训练数据包嵌入的下一代NIDS
FastPacket: Towards Pre-trained Packets Embedding based on FastText for next-generation NIDS
AI总结：
本文提出FastPacket方法，基于字符级嵌入和FastText灵感实现数据包嵌入，用于下一代NIDS，旨在通过大数据预训练模型提升复杂攻击检测能力。
摘要：
攻击者每天都在越来越多地使用新攻击，但许多攻击并未被入侵检测系统（IDS）检测到，因为大多数IDS忽略了原始数据包信息，只关心从PCAP文件中提取的一些基本统计信息。使用网络程序从数据包中提取固定的统计特征是好的，但可能不足以检测当今的挑战。我们认为，现在是利用大数据和深度学习从数据包中自动提取动态特征的时候了。现在是时候从计算机视觉和自然语言处理中的深度学习预训练模型中获取灵感了，这样安全深度学习解决方案将拥有大数据集上的预训练模型，用于未来的研究。在本文中，我们提出了一种基于字符级嵌入的数据包嵌入新方法，其灵感来自FastText在文本数据上的成功。我们称这种方法为FastPacket。结果在CIC-IDS-2017数据集的子集上进行了测量，但我们期望在大数据预训练模型上取得有希望的结果。我们建议在MAWI大数据集上构建预训练的FastPacket，并将其提供给社区，类似于FastText。为了能够超越目前使用的NIDS，开启一个能够更好地检测复杂攻击的数据包级NIDS新时代。
英文摘要：
New Attacks are increasingly used by attackers everyday but many of them are not detected by Intrusion Detection Systems as most IDS ignore raw packet information and only care about some basic statistical information extracted from PCAP files. Using networking programs to extract fixed statistical features from packets is good, but may not enough to detect nowadays challenges. We think that it is time to utilize big data and deep learning for automatic dynamic feature extraction from packets. It is time to get inspired by deep learning pre-trained models in computer vision and natural language processing, so security deep learning solutions will have its pre-trained models on big datasets to be used in future researches. In this paper, we proposed a new approach for embedding packets based on character-level embeddings, inspired by FastText success on text data. We called this approach FastPacket. Results are measured on subsets of CIC-IDS-2017 dataset, but we expect promising results on big data pre-trained models. We suggest building pre-trained FastPacket on MAWI big dataset and make it available to community, similar to FastText. To be able to outperform currently used NIDS, to start a new era of packet-level NIDS that can better detect complex attacks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
