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Open problems in the security of learning
2008
Proceedings of the 1st ACM workshop on Workshop on AISec - AISec '08
Machine learning has become a valuable tool for detecting and preventing malicious activity. However, as more applications employ machine learning techniques in adversarial decision-making situations, increasingly powerful attacks become possible against machine learning systems. In this paper, we present three broad research directions towards the end of developing truly secure learning. First, we suggest that finding bounds on adversarial influence is important to understand the limits of
doi:10.1145/1456377.1456382
dblp:conf/ccs/BarrenoBCJNRST08
fatcat:4uk7kufh4zevfgxkvhz7t4qvm4