Summary of Classification Under Strategic Adversary Manipulation Using Pessimistic Bilevel Optimisation, by David Benfield et al.
Classification under strategic adversary manipulation using pessimistic bilevel optimisation
by David Benfield, Stefano Coniglio, Martin Kunc, Phan Tu Vuong, Alain Zemkoho
First submitted to arxiv on: 26 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Optimization and Control (math.OC)
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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 The proposed adversarial machine learning framework tackles scenarios where learners face attacks from active adversaries, common in applications like spam email filtering and malware detection. The authors model interactions between the learner and adversary as a game, formulating the problem as a pessimistic bilevel optimization problem with the learner as the leader. A stochastic data generator represents the adversary, responding to the classifier. Unlike existing models, this novel approach doesn’t assume the adversary will choose the least costly solution, leading to significant performance improvements. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers developed a new way for machine learning systems to deal with attacks from malicious agents, such as spammers and hackers. They created a game-like scenario where the system tries to classify data while an attacker tries to make it misclassify. This approach is more realistic than previous methods because it doesn’t assume the attacker will always choose the easiest path. As a result, the new method performs much better in tests. |
Keywords
* Artificial intelligence * Machine learning * Optimization
