# eDySec: A Deep Learning-based Explainable Dynamic Analysis Framework for Detecting Malicious Packages in PyPI Ecosystem

arXiv: https://arxiv.org/abs/2604.26219 · HTML: https://mlsanity.com/paper/2604.26219/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 21 of its 63 references that are in the map.
- First posted on arXiv 29 Apr 2026.

## Builds on (most cited first, 21 of 21)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [Time for a change: a tutorial for comparing multiple classifiers through Bayesian analysis](https://mlsanity.com/paper/1606.04316/) (2016)
- [On the Tractability of SHAP Explanations](https://mlsanity.com/paper/2009.08634/) (2020)
- [Explaining the Explainer: A First Theoretical Analysis of LIME](https://mlsanity.com/paper/2001.03447/) (2020)
- [Backstabber's Knife Collection: A Review of Open Source Software Supply Chain Attacks](https://mlsanity.com/paper/2005.09535/) (2020)
- [Adversarial attacks and defenses in explainable artificial intelligence: A survey](https://mlsanity.com/paper/2306.06123/) (2023)
- [An Empirical Study of Malicious Code In PyPI Ecosystem](https://mlsanity.com/paper/2309.11021/) (2023)
- [A Benchmark Comparison of Python Malware Detection Approaches](https://mlsanity.com/paper/2209.13288/) (2022)
- [DONAPI: Malicious NPM Packages Detector using Behavior Sequence Knowledge Mapping](https://mlsanity.com/paper/2403.08334/) (2024)
- [On the Feasibility of Cross-Language Detection of Malicious Packages in npm and PyPI](https://mlsanity.com/paper/2310.09571/) (2023)
- [Explainable Artificial Intelligence (XAI) for Malware Analysis: A Survey of Techniques, Applications, and Open Challenges](https://mlsanity.com/paper/2409.13723/) (2024)
- [MalGuard: Towards Real-Time, Accurate, and Actionable Detection of Malicious Packages in PyPI Ecosystem](https://mlsanity.com/paper/2506.14466/) (2025)
- [Dependable Intrusion Detection System for IoT: A Deep Transfer Learning-based Approach](https://mlsanity.com/paper/2204.04837/) (2022)
- [PyRadar: Towards Automatically Retrieving and Validating Source Code Repository Information for PyPI Packages](https://mlsanity.com/paper/2404.16565/) (2024)
- [A Machine Learning-Based Approach For Detecting Malicious PyPI Packages](https://mlsanity.com/paper/2412.05259/) (2024)
- [DySec: A Machine Learning-based Dynamic Analysis for Detecting Malicious Packages in PyPI Ecosystem](https://mlsanity.com/paper/2503.00324/) (2025)
- [An Empirical Study of Observability Limits in Advanced Software Supply Chain Attacks](https://mlsanity.com/paper/2603.16694/) (2026)
- [Cutting the Gordian Knot: Detecting Malicious PyPI Packages via a Knowledge-Mining Framework](https://mlsanity.com/paper/2601.16463/) (2026)

## Abstract (arXiv)

The security of open-source software repositories is increasingly threatened by next-gen software supply chain attacks. These attacks include multiphase malware execution, remote access activation, and dynamic payload generation. Traditional Machine Learning (ML) detectors struggle to detect these attacks due to the high-dimensional and sparse nature of dynamic behavioral data, including system calls, network traffic, directory access patterns, and dependency logs. As a result, these data characteristics degrade the performance, stability, and explainability of ML models. These challenges have made Deep Learning (DL) a promising alternative, given its success across various domains and its potential for modeling complex patterns. This paper presents eDySec, a DL-based efficient, stable, and explainable framework for dynamic behavioral analysis to detect malicious packages. Using the QUT-DV25 dataset, which captures both install-time and post-installation behaviors of packages, we evaluate DL models and investigate feature sets to identify the most discriminative attributes for enabling efficient malicious package detection. Additionally, model stability analysis and explainable AI techniques are incorporated into the detection pipeline to enable stable, and transparent interpretations of model decisions. Experimental results demonstrate that eDySec significantly outperforms the state-of-the-art frameworks. Specifically, it halves feature dimensionality while lowering false positives by 82% and false negatives by 79%. It also improves accuracy by 3%, achieves near-perfect stability, and maintains an inference latency of 170ms per package. Further analysis reveals that feature and model selection play a critical role, as certain combinations degrade performance. Ultimately, this study advances the understanding of the strengths and limitations of dynamic analysis against next-gen attacks.