# Malware Detection and Prevention using Artificial Intelligence Techniques

arXiv: https://arxiv.org/abs/2206.12770 · HTML: https://mlsanity.com/paper/2206.12770/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 47 references that are in the map.
- First posted on arXiv 26 Jun 2022.

## Builds on (most cited first, 6 of 6)

- [SourceFinder: Finding Malware Source-Code from Publicly Available Repositories](https://mlsanity.com/paper/2005.14311/) (2020)
- [Detection of Advanced Malware by Machine Learning Techniques](https://mlsanity.com/paper/1903.02966/) (2019)
- [A Novel Malware Detection System Based On Machine Learning and Binary Visualization](https://mlsanity.com/paper/1904.00859/) (2019)
- [The Curious Case of Machine Learning In Malware Detection](https://mlsanity.com/paper/1905.07573/) (2019)
- [History of malware](https://mlsanity.com/paper/1302.5392/) (2013)
- [The Program with a Personality: Analysis of Elk Cloner, the First Personal Computer Virus](https://mlsanity.com/paper/2007.15759/) (2020)

## Built on by (most cited first, 7 of 7)

- [Bayesian Hyperparameter Optimization for Deep Neural Network-Based Network Intrusion Detection](https://mlsanity.com/paper/2207.09902/) (2022)
- [Software Supply Chain Vulnerabilities Detection in Source Code: Performance Comparison between Traditional and Quantum Machine Learning Algorithms](https://mlsanity.com/paper/2306.08060/) (2023)
- [EnStack: An Ensemble Stacking Framework of Large Language Models for Enhanced Vulnerability Detection in Source Code](https://mlsanity.com/paper/2411.16561/) (2024)
- [Artificial Intelligence for Cybersecurity: Threats, Attacks and Mitigation](https://mlsanity.com/paper/2209.13454/) (2022)
- [A Novel IoT-based Framework for Non-Invasive Human Hygiene Monitoring using Machine Learning Techniques](https://mlsanity.com/paper/2207.03529/) (2022)
- [Accuracy and Efficiency Trade-Offs in LLM-Based Malware Detection and Explanation: A Comparative Study of Parameter Tuning vs. Full Fine-Tuning](https://mlsanity.com/paper/2511.19654/) (2025)
- [AI-Powered Algorithms for the Prevention and Detection of Computer Malware Infections](https://mlsanity.com/paper/2601.06219/) (2026)

## Abstract (arXiv)

With the rapid technological advancement, security has become a major issue due to the increase in malware activity that poses a serious threat to the security and safety of both computer systems and stakeholders. To maintain stakeholders, particularly, end users security, protecting the data from fraudulent efforts is one of the most pressing concerns. A set of malicious programming code, scripts, active content, or intrusive software that is designed to destroy intended computer systems and programs or mobile and web applications is referred to as malware. According to a study, naive users are unable to distinguish between malicious and benign applications. Thus, computer systems and mobile applications should be designed to detect malicious activities towards protecting the stakeholders. A number of algorithms are available to detect malware activities by utilizing novel concepts including Artificial Intelligence, Machine Learning, and Deep Learning. In this study, we emphasize Artificial Intelligence (AI) based techniques for detecting and preventing malware activity. We present a detailed review of current malware detection technologies, their shortcomings, and ways to improve efficiency. Our study shows that adopting futuristic approaches for the development of malware detection applications shall provide significant advantages. The comprehension of this synthesis shall help researchers for further research on malware detection and prevention using AI.