1+1>2: Integrating Deep Code Behaviors with Metadata Features for Malicious PyPI Package Detection

PyPI, the official package registry for Python, has seen a surge in the number of malicious package uploads in recent years. Prior studies have demonstrated the effectiveness of learning-based solutions in malicious package detection. However, manually-crafted expert rules are expensive and struggle...

Full description

Saved in:
Bibliographic Details
Published in:IEEE/ACM International Conference on Automated Software Engineering : [proceedings] pp. 1159 - 1170
Main Authors: Sun, Xiaobing, Gao, Xingan, Cao, Sicong, Bo, Lili, Wu, Xiaoxue, Huang, Kaifeng
Format: Conference Proceeding
Language:English
Published: ACM 27.10.2024
Subjects:
BERT
Codes
Feature extraction
Malicious Packages
Malware
Metadata
Open-Source Software
Privacy
PyPI
Python
Security
Semantics
Software engineering
Surges
ISSN:2643-1572
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:PyPI, the official package registry for Python, has seen a surge in the number of malicious package uploads in recent years. Prior studies have demonstrated the effectiveness of learning-based solutions in malicious package detection. However, manually-crafted expert rules are expensive and struggle to keep pace with the rapidly evolving malicious behaviors, while deep features automatically extracted from code are still inaccurate in certain cases. To mitigate these issues, in this paper, we propose Ea4mp, a novel approach which integrates deep code behaviors with metadata features to detect malicious PyPI packages. Specifically, Ea4mp extracts code behavior sequences from all script files and fine-tunes a BERT model to learn deep semantic features of malicious code. In addition, we realize the value of metadata information and construct an ensemble classifier to combine the strengths of deep code behavior features and metadata features for more effective detection. We evaluated Ea4mp against three state-of-the-art baselines on a newly constructed dataset. The experimental results show that Ea4mp improves precision by 6.9%-24.6% and recall by 10.5%-18.4%. With Ea4mp, we successfully identified 119 previously unknown malicious packages from a pool of 46,573 newly-uploaded packages over a three-week period, and 82 out of them have been removed by the PyPI official.CCS CONCEPTS* Security and privacy → Malware and its mitigation.
ISSN:2643-1572
DOI:10.1145/3691620.3695493