Can Machine Learning Be Secure? Marco Barreno, Blaine Nelson, Russell Sears, Anthony D. Joseph, J. D. Tygar This paper explores whether machine learning can be secure. It presents a framework for answering the question, "Can machine learning be secure?" and discusses various types of attacks on machine learning systems, defenses against these attacks, and the security implications of using machine learning. The paper also introduces an analytical model that provides a lower bound on the attacker's work function and lists open problems in the field. Machine learning systems are increasingly used in applications such as intrusion detection and spam filtering. However, these systems can be targeted by malicious adversaries. The paper categorizes different types of attacks on machine learning systems, including causative and exploratory attacks, and discusses their impact on system performance. It also explores potential defenses, such as robustness techniques, detecting attacks, and disinformation strategies. The paper also discusses the security implications of using machine learning, including the potential for adversaries to exploit properties of machine learning techniques to disrupt systems. It highlights the importance of considering the trade-offs between expressivity and constraint in learning algorithms and the need for robustness against adversarial attacks. The paper presents a theoretical model that examines a causative attack on a naive learning algorithm. It provides an analytical model that gives a lower bound on the effort required for an adversary to achieve their objective. The model also discusses the trade-offs between using a large number of attack points or extending the attack over many iterations. The paper concludes by discussing related work and research directions, including the need for quantitative measurement of attack effects, security proofs, and detecting adversaries. It emphasizes the importance of understanding the security of machine learning systems and the need for further research in this area.Can Machine Learning Be Secure? Marco Barreno, Blaine Nelson, Russell Sears, Anthony D. Joseph, J. D. Tygar This paper explores whether machine learning can be secure. It presents a framework for answering the question, "Can machine learning be secure?" and discusses various types of attacks on machine learning systems, defenses against these attacks, and the security implications of using machine learning. The paper also introduces an analytical model that provides a lower bound on the attacker's work function and lists open problems in the field. Machine learning systems are increasingly used in applications such as intrusion detection and spam filtering. However, these systems can be targeted by malicious adversaries. The paper categorizes different types of attacks on machine learning systems, including causative and exploratory attacks, and discusses their impact on system performance. It also explores potential defenses, such as robustness techniques, detecting attacks, and disinformation strategies. The paper also discusses the security implications of using machine learning, including the potential for adversaries to exploit properties of machine learning techniques to disrupt systems. It highlights the importance of considering the trade-offs between expressivity and constraint in learning algorithms and the need for robustness against adversarial attacks. The paper presents a theoretical model that examines a causative attack on a naive learning algorithm. It provides an analytical model that gives a lower bound on the effort required for an adversary to achieve their objective. The model also discusses the trade-offs between using a large number of attack points or extending the attack over many iterations. The paper concludes by discussing related work and research directions, including the need for quantitative measurement of attack effects, security proofs, and detecting adversaries. It emphasizes the importance of understanding the security of machine learning systems and the need for further research in this area.