# Black-Box Attacks against RNN based Malware Detection Algorithms

arXiv: https://arxiv.org/abs/1705.08131 · HTML: https://mlsanity.com/paper/1705.08131/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 May 2017.

## Built on by (most cited first, 13 of 13)

- [Adversarial Attacks against Windows PE Malware Detection: A Survey of the State-of-the-Art](https://mlsanity.com/paper/2112.12310/) (2021)
- [RS-Del: Edit Distance Robustness Certificates for Sequence Classifiers via Randomized Deletion](https://mlsanity.com/paper/2302.01757/) (2023)
- [EvadeDroid: A Practical Evasion Attack on Machine Learning for Black-box Android Malware Detection](https://mlsanity.com/paper/2110.03301/) (2021)
- [A Survey on Malware Detection with Graph Representation Learning](https://mlsanity.com/paper/2303.16004/) (2023)
- [Improving the robustness and accuracy of biomedical language models through adversarial training](https://mlsanity.com/paper/2111.08529/) (2021)
- [Explanation-Guided Diagnosis of Machine Learning Evasion Attacks](https://mlsanity.com/paper/2106.15820/) (2021)
- [Spatially Focused Attack against Spatiotemporal Graph Neural Networks](https://mlsanity.com/paper/2109.04608/) (2021)
- [Single-Shot Black-Box Adversarial Attacks Against Malware Detectors: A Causal Language Model Approach](https://mlsanity.com/paper/2112.01724/) (2021)
- [Emerging Threats in Deep Learning-Based Autonomous Driving: A Comprehensive Survey](https://mlsanity.com/paper/2210.11237/) (2022)
- [LSDAT: Low-Rank and Sparse Decomposition for Decision-based Adversarial Attack](https://mlsanity.com/paper/2103.10787/) (2021)
- [Minerva: A File-Based Ransomware Detector](https://mlsanity.com/paper/2301.11050/) (2023)
- [Art-Attack: Black-Box Adversarial Attack via Evolutionary Art](https://mlsanity.com/paper/2203.04405/) (2022)
- [Adversarial Attacks on Transformers-Based Malware Detectors](https://mlsanity.com/paper/2210.00008/) (2022)

## Abstract (arXiv)

Recent researches have shown that machine learning based malware detection algorithms are very vulnerable under the attacks of adversarial examples. These works mainly focused on the detection algorithms which use features with fixed dimension, while some researchers have begun to use recurrent neural networks (RNN) to detect malware based on sequential API features. This paper proposes a novel algorithm to generate sequential adversarial examples, which are used to attack a RNN based malware detection system. It is usually hard for malicious attackers to know the exact structures and weights of the victim RNN. A substitute RNN is trained to approximate the victim RNN. Then we propose a generative RNN to output sequential adversarial examples from the original sequential malware inputs. Experimental results showed that RNN based malware detection algorithms fail to detect most of the generated malicious adversarial examples, which means the proposed model is able to effectively bypass the detection algorithms.