# Evolution and Detection of Polymorphic and Metamorphic Malwares: A Survey

arXiv: https://arxiv.org/abs/1406.7061 · HTML: https://mlsanity.com/paper/1406.7061/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jun 2014.

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

- [Explainable Intrusion Detection Systems (X-IDS): A Survey of Current Methods, Challenges, and Opportunities](https://mlsanity.com/paper/2207.06236/) (2022)
- [Robust Android Malware Detection System against Adversarial Attacks using Q-Learning](https://mlsanity.com/paper/2101.12031/) (2021)
- [Malware Analysis with Artificial Intelligence and a Particular Attention on Results Interpretability](https://mlsanity.com/paper/2107.11100/) (2021)
- [Creating an Explainable Intrusion Detection System Using Self Organizing Maps](https://mlsanity.com/paper/2207.07465/) (2022)
- [Explainable Intrusion Detection Systems Using Competitive Learning Techniques](https://mlsanity.com/paper/2303.17387/) (2023)
- [Cluster Analysis and Concept Drift Detection in Malware](https://mlsanity.com/paper/2502.14135/) (2025)
- [MalTree: Tracing Malware Evolution from Embeddings at Scale](https://mlsanity.com/paper/2606.06570/) (2026)

## Abstract (arXiv)

Malwares are big threat to digital world and evolving with high complexity. It can penetrate networks, steal confidential information from computers, bring down servers and can cripple infrastructures etc. To combat the threat/attacks from the malwares, anti- malwares have been developed. The existing anti-malwares are mostly based on the assumption that the malware structure does not changes appreciably. But the recent advancement in second generation malwares can create variants and hence posed a challenge to anti-malwares developers. To combat the threat/attacks from the second generation malwares with low false alarm we present our survey on malwares and its detection techniques.