A Categorical Data Approach for Anomaly Detection in WebAssembly Applications

Tiago Heinrich, Newton Will, Rafael Obelheiro, Carlos Maziero

2024

Abstract

The security of Web Services for users and developers is essential; since WebAssembly is a new format that has gained attention in this type of environment over the years, new measures for security are important. However, intrusion detection solutions for WebAssembly applications are generally limited to static binary analysis. We present a novel approach for dynamic WebAssembly intrusion detection, using data categorization and machine learning. Our proposal analyses communication data extracted from the WebAssembly sandbox, with the goal of better capturing the applications’ behavior. Our approach was validated using two strategies, online and offline, to assess the effectiveness of categorical data for intrusion detection. The obtained results show that both strategies are feasible for WebAssembly intrusion detection, with a high detection rate and low false negative and false positive rates.

Paper Citation

in Harvard Style

Heinrich T., Will N., Obelheiro R. and Maziero C. (2024). A Categorical Data Approach for Anomaly Detection in WebAssembly Applications. In Proceedings of the 10th International Conference on Information Systems Security and Privacy - Volume 1: ICISSP; ISBN 978-989-758-683-5, SciTePress, pages 275-284. DOI: 10.5220/0012252800003648

in Bibtex Style

@conference{icissp24,
author={Tiago Heinrich and Newton Will and Rafael Obelheiro and Carlos Maziero},
title={A Categorical Data Approach for Anomaly Detection in WebAssembly Applications},
booktitle={Proceedings of the 10th International Conference on Information Systems Security and Privacy - Volume 1: ICISSP},
year={2024},
pages={275-284},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012252800003648},
isbn={978-989-758-683-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 10th International Conference on Information Systems Security and Privacy - Volume 1: ICISSP
TI - A Categorical Data Approach for Anomaly Detection in WebAssembly Applications
SN - 978-989-758-683-5
AU - Heinrich T.
AU - Will N.
AU - Obelheiro R.
AU - Maziero C.
PY - 2024
SP - 275
EP - 284
DO - 10.5220/0012252800003648
PB - SciTePress