# The State-of-the-Art in AI-Based Malware Detection Techniques: A Review

arXiv: https://arxiv.org/abs/2210.11239 · HTML: https://mlsanity.com/paper/2210.11239/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 46 references that are in the map.
- First posted on arXiv 12 Oct 2022.

## Builds on (most cited first, 7 of 7)

- [MaMaDroid: Detecting Android Malware by Building Markov Chains of Behavioral Models](https://mlsanity.com/paper/1612.04433/) (2016)
- [Automated Poisoning Attacks and Defenses in Malware Detection Systems: An Adversarial Machine Learning Approach](https://mlsanity.com/paper/1706.04146/) (2017)
- [A Novel Malware Detection System Based On Machine Learning and Binary Visualization](https://mlsanity.com/paper/1904.00859/) (2019)
- [MalDetConv: Automated Behaviour-based Malware Detection Framework Based on Natural Language Processing and Deep Learning Techniques](https://mlsanity.com/paper/2209.03547/) (2022)
- [BEBP: An Poisoning Method Against Machine Learning Based IDSs](https://mlsanity.com/paper/1803.03965/) (2018)
- [SeqNet: An Efficient Neural Network for Automatic Malware Detection](https://mlsanity.com/paper/2205.03850/) (2022)
- [Machine Learning-based Ransomware Detection Using Low-level Memory Access Patterns Obtained From Live-forensic Hypervisor](https://mlsanity.com/paper/2205.13765/) (2022)

## Built on by (most cited first, 1 of 1)

- [A Novel Approach to Malicious Code Detection Using CNN-BiLSTM and Feature Fusion](https://mlsanity.com/paper/2410.09401/) (2024)

## Abstract (arXiv)

Artificial Intelligence techniques have evolved rapidly in recent years, revolutionising the approaches used to fight against cybercriminals. But as the cyber security field has progressed, so has malware development, making it an economic imperative to strengthen businesses' defensive capability against malware attacks. This review aims to outline the state-of-the-art AI techniques used in malware detection and prevention, providing an in-depth analysis of the latest studies in this field. The algorithms investigated consist of Shallow Learning, Deep Learning and Bio-Inspired Computing, applied to a variety of platforms, such as PC, cloud, Android and IoT. This survey also touches on the rapid adoption of AI by cybercriminals as a means to create ever more advanced malware and exploit the AI algorithms designed to defend against them.