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

Yinzheng Zhong; Alexei Lisitsa

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Paper

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

Topics: AI and Machine Learning for Security; Intrusion Detection and Response; Malware Detection

In Proceedings of the 9th International Conference on Information Systems Security and Privacy ICISSP - Volume 1, 576-582, 2023 , Lisbon, Portugal

Authors: Yinzheng Zhong and Alexei Lisitsa

Affiliation: Department of Computer Science, University of Liverpool, Liverpool, U.K.

Keyword(s): Intrusion Detection Systems, Process Mining, Machine Learning.

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.

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Paper citation in several formats:

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 - ICISSP; ISBN 978-989-758-624-8; ISSN 2184-4356, SciTePress, pages 576-582. DOI: 10.5220/0011726700003405

@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 - ICISSP},
year={2023},
pages={576-582},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011726700003405},
isbn={978-989-758-624-8},
issn={2184-4356},
}

TY - CONF

JO - Proceedings of the 9th International Conference on Information Systems Security and Privacy - ICISSP
TI - Online Transition-Based Feature Generation for Anomaly Detection in Concurrent Data Streams
SN - 978-989-758-624-8
IS - 2184-4356
AU - Zhong, Y.
AU - Lisitsa, A.
PY - 2023
SP - 576
EP - 582
DO - 10.5220/0011726700003405
PB - SciTePress