论文标题
攻击和捍卫公共云的机器学习应用
Attacking and Defending Machine Learning Applications of Public Cloud
论文作者
论文摘要
对抗性攻击打破了传统安全防御的界限。对于对抗性攻击和云服务的特征，我们建议用于机器学习应用程序的安全开发生命周期，例如ML的SDL。 ML的SDL通过减少ML-AS-A-Service中漏洞的数量和严重性来帮助开发人员构建更安全的软件，同时降低开发成本。
Adversarial attack breaks the boundaries of traditional security defense. For adversarial attack and the characteristics of cloud services, we propose Security Development Lifecycle for Machine Learning applications, e.g., SDL for ML. The SDL for ML helps developers build more secure software by reducing the number and severity of vulnerabilities in ML-as-a-service, while reducing development cost.
