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

**Authors:** Ashu Sharma, S. K. Sahay  
**arXiv:** [1406.7061](https://arxiv.org/abs/1406.7061)  
**Date:** 2014-06-27  
**Summary page:** https://www.summarizepaper.com/en/paper/1406.7061v2/

## Key points

- Malwares are a significant threat in the digital world that constantly evolves with high complexity
- Malicious software programs can penetrate networks, steal confidential information, bring down servers, and cripple infrastructures
- Anti-malware software has been developed to combat these threats and attacks from malwares
- Existing anti-malware software is mostly based on the assumption that malware structure does not change appreciably, which has been challenged by recent advancements in second-generation malwares that can create variants
- Ashu Sharma and S.K. Sahay conducted a survey on malwares and its detection techniques to address this challenge of detecting second-generation malwares with low false alarms
- The authors present an overview of polymorphic and metamorphic malwares - two types of second-generation malwares that are particularly challenging to detect
- Various techniques for detecting these types of malwares are discussed including signature-based detection, behavior-based detection, heuristics-based detection, sandboxing techniques, machine learning-based detection methods, and hybrid approaches
- Limitations of each technique are highlighted and future research directions for improving malware detection accuracy while minimizing false positives are suggested
- This survey provides valuable insights into the evolution of malwares and the challenges associated with detecting them
- There is a need for more sophisticated anti-malware solutions that can keep up with the ever-evolving threat landscape in the digital world.

## Summary

In the digital world, malwares are a significant threat that is constantly evolving with high complexity. These malicious software programs can penetrate networks, steal confidential information from computers, bring down servers, and cripple infrastructures. To combat these threats and attacks from malwares, anti-malware software has been developed. However, the existing anti-malware software is mostly based on the assumption that the malware structure does not change appreciably. This assumption has been challenged by recent advancements in second-generation malwares that can create variants and pose a challenge to anti-malware developers. To address this challenge of detecting second-generation malwares with low false alarms, Ashu Sharma and S.K. Sahay have conducted a survey on malwares and its detection techniques. In their paper titled "Evolution and Detection of Polymorphic and Metamorphic Malwares: A Survey," they present an overview of polymorphic and metamorphic malwares - two types of second-generation malwares that are particularly challenging to detect. The authors discuss various techniques for detecting these types of malwares, including signature-based detection, behavior-based detection, heuristics-based detection, sandboxing techniques, machine learning-based detection methods, and hybrid approaches. They also highlight the limitations of each technique and suggest future research directions for improving malware detection accuracy while minimizing false positives. Overall, this survey provides valuable insights into the evolution of malwares and the challenges associated with detecting them. It highlights the need for more sophisticated anti-malware solutions that can keep up with the ever-evolving threat landscape in the digital world.