In the digital age, cyber-threats are a growing concern for individuals, businesses, and governments alike. These threats can range from data breaches and identity theft to large-scale attacks on critical infrastructure. The consequences of such attacks can be severe, leading to financial losses, threats to national security, and the loss of lives. This paper presents a holistic approach to increase the security of critical infrastructures. For that, we propose an open, self-configurable, and AI-based automated cyber-defense platform that runs on specifically hardened devices and own hardware, can be deeply embedded in critical infrastructures and provides full visibility on network, endpoints, and software. In this paper, starting from a thorough analysis of related work, we describe the vision of our SOVEREIGN platform in the form of an architecture, discuss individual building blocks, and evaluate it qualitatively with respect to our requirements.

When developing networking systems such as firewalls, routers, and intrusion detection systems, one faces a striking gap between the ease with which one can often describe a desired analysis in high-level terms, and the tremendous amount of low-level implementation details that one must still grapple with to come to a robust solution. We present HILTI, a platform that bridges this divide by providing to application developers much of the low-level functionality, without tying it to a specific analysis structure. HILTI consists of two parts: (1) an abstract machine model that we tailor specifically to the networking domain, directly supporting the field's common abstractions and idioms in its instruction set; and (2) a compilation strategy for turning programs written for the abstract machine into optimized, natively executable code. We have developed a prototype of the HILTI compiler toolchain that fully implements the design's functionality, and ported exemplars of networking applications to the HILTI model to demonstrate the aptness of its abstractions. Our evaluation of HILTI's functionality and performance confirms its potential to become a powerful platform for future application development.

When an organization detects a security breach, it undertakes a forensic analysis to figure out what happened. This investigation involves inspecting a wide range of heterogeneous data sources spanning over a long period of time. The iterative nature of the analysis procedure requires an interactive experience with the data. However, the distributed processing paradigms we find in practice today fail to provide this requirement: the batch-oriented nature of MapReduce cannot deliver sub-second round-trip times, and distributed in-memory processing cannot store the terabytes of activity logs needed to inspect during an incident.

Much of the Internet's end-to-end security relies on the SSL/TLS protocol along with its underlying X.509 certificate infrastructure. However, the system remains quite brittle due to its liberal delegation of signing authority: a single compromised certification authority undermines trust globally. Several recent high-profile incidents have demonstrated this shortcoming convincingly. Over time, the security community has proposed a number of counter measures to increase the security of the certificate ecosystem; many of these efforts monitor for what they consider tell-tale signs of man-in-the-middle attacks. In this work we set out to understand to which degree benign changes to the certificate ecosystem share structural properties with attacks, based on a large-scale data set of more than 17 billion SSL sessions. We find that common intuition falls short in assessing the maliciousness of an unknown certificate, since their typical artifacts routinely occur in benign contexts as well. We also discuss what impact our observations have on proposals aiming to improve the security of the SSL ecosystem.

When browsers report TLS errors, they cannot distinguish between attacks and harmless server misconfigurations; hence they leave it to the user to decide whether continuing is safe. However, actual attacks remain rare. As a result, users quickly become used to "false positives" that deplete their attention span, making it unlikely that they will pay sufficient scrutiny when a real attack comes along. Consequently, browser vendors should aim to minimize the number of low-risk warnings they report. To guide that process, we perform a large-scale measurement study of common TLS warnings. Using a set of passive network monitors located at different sites, we identify the prevalence of warnings for a total population of about 300,000 users over a nine-month period. We identify low-risk scenarios that consume a large chunk of the user attention budget and make concrete recommendations to browser vendors that will help maintain user attention in high-risk situations. We study the impact on end users with a data set much larger in scale than the data sets used in previous TLS measurement studies. A key novelty of our approach involves the use of internal browser code instead of generic TLS libraries for analysis, providing more accurate and representative results.