Global Bigdata Conference

As government agencies are beginning to turn over security to automated systems that can teach themselves, the idea that hackers can sneakily influence those systems is becoming the latest (and perhaps the greatest) new concern for cybersecurity professionals. Adversarial machine learning is a research field that "lies at the intersection of machine learning and computer security," according to Wikipedia. "It aims to enable the safe adoption of machine-learning techniques in adversarial settings like spam filtering, malware detection and biometric recognition." According to Nicolas Papernot, Google PhD Fellow in Security at Pennsylvania State University, AML seeks to better understand the behavior of machine-learning algorithms once they are deployed in adversarial settings -- that is, "any setting where the adversary has an incentive, may it be financial or of some other nature, to force the machine-learning algorithms to misbehave." "Unfortunately, current machine-learning models have a large attack surface as they were designed and trained to have good average performance, but not necessarily worst-case performance, which is typically what is sought after from a security perspective," Papernot said.