Insecure default values in software settings can be exploited by attackers to compromise the system that runs the software. As a countermeasure, there exist security-configuration guides specifying in detail which values are secure. However, most administrators still refrain from hardening existing systems because the system functionality is feared to deteriorate if secure settings are applied. To foster the application of security-configuration guides, it is necessary to identify those rules that would restrict the functionality.This article presents our approach to use combinatorial testing to find problematic combinations of rules and machine learning techniques to identify the problematic rules within these combinations. The administrators can then apply only the unproblematic rules and, therefore, increase the system’s security without the risk of disrupting its functionality. To demonstrate the usefulness of our approach, we applied it to real-world problems drawn from discussions with administrators at Siemens and found the problematic rules in these cases. We hope that this approach and its open-source implementation motivate more administrators to harden their systems and, thus, increase their systems’ general security.

Security Hardening is the process of configuring IT systems to ensure the security of the systems' components and data they process or store. In many cases, so-called security-configuration guides are used as a basis for security hardening. These guides describe secure configuration settings for components such as operating systems and standard applications. Rigorous testing of security-configuration guides and automated mechanisms for their implementation and validation are necessary since erroneous implementations or checks of hardening guides may severely impact systems' security and functionality. At Siemens, centrally maintained security-configuration guides carry machine-readable information specifying both the implementation and validation of each required configuration step. The guides are maintained within git repositories; automated pipelines generate the artifacts for implementation and checking, e.g., PowerShell scripts for Windows, and carry out testing of these artifacts on AWS images. This paper describes our experiences with our DevOps-inspired approach for authoring, maintaining, and testing security-configuration guides. We want to share these experiences to help other organizations with their security hardening and increase their systems' security.

To secure computer infrastructure, we need to configure all security-relevant settings. We need security experts to identify security-relevant settings, but this process is time-consuming and expensive. Our proposed solution uses state-of-the-art natural language processing to classify settings as security-relevant based on their description. Our evaluation shows that our trained classifiers do not perform well enough to replace the human security experts but can help them classify the settings. By publishing our labeled data sets and the code of our trained model, we want to help security experts analyze configuration settings and enable further research in this area.

Hardening is the process of configuring IT systems to ensure the security of the systems' components and data they process or store. The complexity of contemporary IT infrastructures, however, renders manual security hardening and maintenance a daunting task.

Understanding and fending off attack campaigns against organizations, companies and individuals, has become a global struggle. As today's threat actors become more determined and organized, isolated efforts to detect and reveal threats are no longer effective. Although challenging, this situation can be significantly changed if information about security incidents is collected, shared and analyzed across organizations. To this end, different exchange data formats such as STIX, CyBOX, or IODEF have been recently proposed and numerous CERTs are adopting these threat intelligence standards to share tactical and technical threat insights. However, managing, analyzing and correlating the vast amount of data available from different sources to identify relevant attack patterns still remains an open problem.

A cost recurrence describes an upper bound for the running time of a program in terms of the size of its input. Finding cost recurrences is a frequent intermediate step in complexity analysis, and this step requires an abstraction from data to data size. In this article, we use information contained in dependent types to achieve such an abstraction: Dependent ML (DML), a conservative extension of ML, provides dependent types that can be used to associate data with size information, thus describing a possible abstraction. We automatically extract cost recurrences from first-order DML programs, guiding the abstraction from data to data size with information contained in DML type derivations.

The second Futamura projection describes the automatic generation of non-trivial generating extensions by applying a partial evaluator to itself. We derive an ML implementation of the second Futamura projection for Type-Directed Partial Evaluation (TDPE). Due to the differences between `traditional', syntax-directed partial evaluation and TDPE, this derivation involves several conceptual and technical steps. These include a suitable formulation of the second Futamura projection and techniques for using TDPE to specialize type-indexed programs. In the context of the second Futamura projection, we also compare and relate TDPE with conventional offline partial evaluation.