Summary of A New Formulation For Zeroth-order Optimization Of Adversarial Exemples in Malware Detection, by Marco Rando et al.
A New Formulation for Zeroth-Order Optimization of Adversarial EXEmples in Malware Detection
by Marco Rando, Luca Demetrio, Lorenzo Rosasco, Fabio Roli
First submitted to arxiv on: 23 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Machine learning malware detectors are vulnerable to adversarial examples, which can evade detection by crafting Windows programs that exploit weaknesses in the detectors. To address this challenge, researchers typically rely on heuristic algorithms that inject new content into legitimate programs. However, these approaches often lack theoretical guarantees and require significant hyperparameter tuning. In contrast, a zeroth-order optimization framework allows for the deployment of efficient gradient-free optimization algorithms with minimal hyperparameters tuning. This framework is particularly well-suited for addressing the functionality-preserving constraint required in adversarial malware detection. The paper proposes ZEXE, a novel zero-order attack against Windows malware detection that achieves drastic improvements in evasion rates while reducing the size of injected content by two-thirds. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Malware detectors are like superheroes that protect our computers from bad guys. But some sneaky villains have found ways to trick these heroes and make their programs look legitimate. This is a big problem because it makes it hard for the detectors to catch the bad guys. To solve this challenge, researchers need new ideas that can help them create better detectors. A zeroth-order optimization framework is like a superpower that lets them use algorithms that are fast and efficient. It’s also very good at making sure the bad guys don’t get away with their tricks. The paper proposes a new way to attack Windows malware detection called ZEXE, which is really good at evading detection while being small. |
Keywords
» Artificial intelligence » Hyperparameter » Machine learning » Optimization
