Year: 2023
Type: preprint
Abstract: Typical event datasets such as those used in network intrusion detection comprise hundreds of thousands, sometimes millions, of discrete packet events. These datasets tend to be high dimensional, stateful, and time-series in nature, holding complex local and temporal feature associations. Packet data can be abstracted into lower dimensional summary data, such as packet flow records, where some of the temporal complexities of packet data can be mitigated, and smaller well-engineered feature subsets can be created. This data can be invaluable as training data for machine learning and cyber threat detection techniques. Data can be collected in real-time, or from historical packet trace archives. In this paper we focus on how flow records and summary metadata can be extracted from packet data with high accuracy and robustness. We identify limitations in current methods, how they may impact datasets, and how these flaws may impact learning models. Finally, we propose methods to improve the state of the art and introduce proof of concept tools to support this work.
Source: arXiv (Cornell University)
Authors: Kenyon, Anthony, Elizondo, David, Deka, Lipika
Institution: De Montfort University
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4387724175