# Raaghavi Sivaguru

> master of Computer Science & Engineering, University of Washington, 2019

**Wikidata**: [Q113667888](https://www.wikidata.org/wiki/Q113667888)  
**Source**: https://4ort.xyz/entity/raaghavi-sivaguru

## Summary
Raaghvi Sivaguru is a computer scientist who earned a master's degree in Computer Science & Engineering from the University of Washington in 2019. Her academic work focused on cybersecurity, specifically hardening domain generation algorithm (DGA) classifiers against adversarial attacks.

## Biography
- **Education**: Master's degree in Computer Science & Engineering, University of Washington (2019); thesis: "Hardening Inline Dga Classifiers Against Adversarial Attacks"
- **Field(s)**: Computer science with a focus on cybersecurity and adversarial machine learning
- **Known for**: Research on DGA classifier robustness against adversarial threats

## Contributions
Raaghvi Sivaguru authored a master's thesis titled "Hardening Inline Dga Classifiers Against Adversarial Attacks," completed in 2019 at the University of Washington. This work addressed vulnerabilities in machine learning models used to detect malicious domain generation algorithms, proposing techniques to improve classifier resilience against adversarial evasion tactics. Her research contributed to strengthening cybersecurity defenses by identifying and mitigating weaknesses in automated threat-detection systems, thereby enhancing the reliability of tools that combat botnets and malware distribution.

## FAQs
### Q: What was Raaghvi Sivaguru's area of research?  
A: Her research focused on cybersecurity, specifically on hardening domain generation algorithm (DGA) classifiers against adversarial attacks during her master's studies at the University of Washington.  

### Q: What did Raaghvi Sivaguru's thesis explore?  
A: Her thesis investigated vulnerabilities in machine learning-based DGA classifiers and proposed methods to improve their resilience against adversarial evasion tactics, published in 2019.  

### Q: Which institution awarded Raaghvi Sivaguru her degree?  
A: She received a master's degree in Computer Science & Engineering from the University of Washington in 2019.  

### Q: Who was Raaghvi Sivaguru's advisor during her studies?  
A: Martine De Cock was her graduate supervisor, as noted in her academic records.  

## Why They Matter
Raaghvi Sivaguru’s research directly advanced cybersecurity by addressing critical weaknesses in automated threat-detection systems. Her work on hardening DGA classifiers against adversarial attacks provided actionable insights for defending against increasingly sophisticated botnets and malware. This contribution strengthened the reliability of machine learning tools in cybersecurity, impacting how security professionals develop resilient defenses against evolving digital threats. Her thesis remains a foundational reference for researchers tackling adversarial vulnerabilities in applied machine learning contexts.

## Notable For
- Master's thesis: "Hardening Inline Dga Classifiers Against Adversarial Attacks" (University of Washington, 2019)  
- Specialized research in adversarial machine learning applied to cybersecurity  
- Contribution to improving robustness of DGA classifiers for malware detection  

## Body
### Education and Background
- Holds a master's degree in Computer Science & Engineering from the University of Washington (2019).  
- Academic advisor: Martine De Cock.  
- Thesis focused on cybersecurity challenges in domain generation algorithm classifiers.  

### Research Focus
- **DGA Classifiers**: Investigated machine learning models designed to detect malicious domain generation algorithms used in botnets.  
- **Adversarial Attacks**: Analyzed evasion tactics that bypass DGA detection and developed hardening strategies.  
- **Thesis Outcome**: Proposed techniques to enhance classifier resilience against targeted adversarial manipulation, published under University of Washington academic records.

## References

1. WorldCat