In this chapter, we present a brief overview of important topics regarding the connection of data science and security. In the first part, we focus on the security of data science and discuss a selection of security aspects that data scientists should consider to make their services and products more secure. In the second part about security for data science, we switch sides and present some applications where data science plays a critical role in pushing the state-of-the-art in securing information systems. This includes a detailed look at the potential and challenges of applying machine learning to the problem of detecting obfuscated JavaScripts. Key Concepts of Information SecurityAccording to the Federal Information Security Management Act of 2002 (2012), the term "information security" means protecting information and information systems from unauthorized access, use, disclosure, disruption, modification, or destruction in order to provide confidentiality, integrity, and availability (CIA):

We propose Downright, a novel framework based on Seccomp, Berkeley Packet Filter, and PTrace, that makes it possible to equip new and existing C applications with a request broker architecture. An extensive configuration language allows AppArmor-like configuration that supports programmers in building rules for system call parameter validation and result sanitization. Access to these privileged function calls can be restricted both within Linux kernel and user spaces. Downright's main strength compared to related approaches is that it implements a complete mediation request broker architecture, in which all system calls are vetted before execution, either by the kernel or by a request broker, which runs as another process. This isolates the main program from many failures due to programming bugs and attacks, which would have to pass not only the attacked program, but the request broker also. We argue that this makes acquiring and releasing elevated privileges easier and safer. Downright eliminates the need to write Seccomp programs, instead allowing policies to be expressed declaratively through a rich policy language. We demonstrate the viability of this approach by instrumenting nginx, an industrial-strength web server and reverse proxy. While this instrumentation takes only a single line of code, we argue that even this effort can be avoided by suitable C runtime code. We show that Downright's overhead is substantial, halving nginx's perfomance, but propose measures for optimisation.

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