Challenges in the Use of ML for Security

This chapter discusses major challenges in the use of ML for security, which are very much the same as in other domains. The chapter covers issues related to training datasets, such as scarcity of training data, and dataset quality. It also covers ethics issues and discusses several key dimensions of ethics, namely explainability, fairness, robustness, transparency, and privacy. The chapter then concludes with a discussion about ML security. Because many such topics are being extensively investigated, the chapter also provides pointers to surveys and tutorial papers, where the reader can find technical details, classifications of proposed approaches, and further references.