# Security Evaluation of Support Vector Machines in Adversarial Environments

arXiv: https://arxiv.org/abs/1401.7727 · HTML: https://mlsanity.com/paper/1401.7727/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jan 2014.

## Built on by (most cited first, 8 of 8)

- [Adversarial Example Detection for DNN Models: A Review and Experimental Comparison](https://mlsanity.com/paper/2105.00203/) (2021)
- [Support Vector Machines under Adversarial Label Contamination](https://mlsanity.com/paper/2206.00352/) (2022)
- [Security and Privacy for Artificial Intelligence: Opportunities and Challenges](https://mlsanity.com/paper/2102.04661/) (2021)
- [Understanding Robust Learning through the Lens of Representation Similarities](https://mlsanity.com/paper/2206.09868/) (2022)
- [A Red Teaming Framework for Securing AI in Maritime Autonomous Systems](https://mlsanity.com/paper/2312.11500/) (2023)
- [A Geometric Method for Improved Uncertainty Estimation in Real-time](https://mlsanity.com/paper/2206.11562/) (2022)
- [Evaluating robustness of support vector machines with the Lagrangian dual approach](https://mlsanity.com/paper/2306.02639/) (2023)
- [Problem space structural adversarial attacks for Network Intrusion Detection Systems based on Graph Neural Networks](https://mlsanity.com/paper/2403.11830/) (2024)

## Abstract (arXiv)

Support Vector Machines (SVMs) are among the most popular classification techniques adopted in security applications like malware detection, intrusion detection, and spam filtering. However, if SVMs are to be incorporated in real-world security systems, they must be able to cope with attack patterns that can either mislead the learning algorithm (poisoning), evade detection (evasion), or gain information about their internal parameters (privacy breaches). The main contributions of this chapter are twofold. First, we introduce a formal general framework for the empirical evaluation of the security of machine-learning systems. Second, according to our framework, we demonstrate the feasibility of evasion, poisoning and privacy attacks against SVMs in real-world security problems. For each attack technique, we evaluate its impact and discuss whether (and how) it can be countered through an adversary-aware design of SVMs. Our experiments are easily reproducible thanks to open-source code that we have made available, together with all the employed datasets, on a public repository.