Online Transition-Based Feature Generation for Anomaly Detection in Concurrent Data Streams

Yinzheng Zhong, Alexei Lisitsa

2023

Abstract

In this paper, we introduce the transition-based feature generator (TFGen) technique, which reads general activity data with attributes and generates step-by-step generated data. The activity data may consist of network activity from packets, system calls from processes or classified activity from surveillance cameras. TFGen processes data online and will generate data with encoded historical data for each incoming activity with high computational efficiency. The input activities may concurrently originate from distinct traces or channels. The technique aims to address issues such as domain-independent applicability, the ability to discover global process structures, the encoding of time-series data, and online processing capability.

Paper Citation

in Harvard Style

Zhong Y. and Lisitsa A. (2023). Online Transition-Based Feature Generation for Anomaly Detection in Concurrent Data Streams. In Proceedings of the 9th International Conference on Information Systems Security and Privacy - Volume 1: ICISSP, ISBN 978-989-758-624-8, pages 576-582. DOI: 10.5220/0011726700003405

in Bibtex Style

@conference{icissp23,
author={Yinzheng Zhong and Alexei Lisitsa},
title={Online Transition-Based Feature Generation for Anomaly Detection in Concurrent Data Streams},
booktitle={Proceedings of the 9th International Conference on Information Systems Security and Privacy - Volume 1: ICISSP,},
year={2023},
pages={576-582},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011726700003405},
isbn={978-989-758-624-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 9th International Conference on Information Systems Security and Privacy - Volume 1: ICISSP,
TI - Online Transition-Based Feature Generation for Anomaly Detection in Concurrent Data Streams
SN - 978-989-758-624-8
AU - Zhong Y.
AU - Lisitsa A.
PY - 2023
SP - 576
EP - 582
DO - 10.5220/0011726700003405