The internet has become a dangerous place: malicious code gets spread on personal computers across the world, creating botnets ready to attack the network infrastructure at any time. Monitoring network traffic and keeping track of the vast number of security incidents or other anomalies in the network are challenging tasks. While monitoring and intrusion detection systems are widely used to collect operational data in real-time, attempts to manually analyze their output at a fine-granular level are often tedious, require exhaustive human resources, or completely fail to provide the necessary insight due to the complexity and the volume of the underlying data. This dissertation represents an effort to complement automatic monitoring and intrusion detection systems with visual exploration interfaces that empower human analysts to gain deeper insight into large, complex, and dynamically changing data sets. In this context, one key aspect of visual analysis is the refinement of existing visualization methods to improve their scalability with respect to a) data volume, b) visual limitations of computer screens, and c) human perception capacities. In addition to that, the development of innovative visualization metaphors for viewing network data is a further key aspect of this thesis. In particular, this dissertation deals with scalable visualization techniques for detailed analysis of large network time series. By grouping time series according to their logical intervals in pixel visualizations and by coloring them for better discrimination, our methods enable accurate comparisons of temporal aspects in network security data sets. In order to reveal the peculiarities of network traffic and distributed attacks with regard to the distribution of the participating hosts, a hierarchical map of the IP address space is proposed, which takes both geographical and topological aspects of the Internet into account. Since visual clutter becomes an issue when naively connecting the major communication partners on top of this map, hierarchical edge bundles are used for grouping traffic links based on the map's hierarchy, thereby facilitating a more scalable analysis of communication partners. Furthermore, the map is complemented by multivariate analysis techniques for visually studying the multidimensional nature of network traffic and security event data. Especially the interaction of the implemented prototypes reveals the applicability of the proposed visualization methods to provide an overview, to relate communication partners, to zoom into regions of interest, and to retrieve detailed information. For an even more detailed analysis of hosts in the network, we introduce a graph-based approach to tracking behavioral changes of hosts and higher-level network entities. This information is particularly useful for detecting misbehaving computers within the local network infrastructure, which can otherwise substantially compromise the security of the network. To complete the comprehensive view on network traffic, a Self-Organizing Map was used to demonstrate the usefulness of visualization methods for analyzing not only structured network protocol data but also unstructured information, e.g., the textual context of email messages. By extracting features from the emails, the neuronal network algorithm clusters similar emails and is capable of distinguishing between spam and legitimate emails up to a certain extent. In the scope of this dissertation, the presented prototypes demonstrate the applicability of the proposed visualization methods in numerous case studies and reveal the exhaustless potential of their usage in combination with automatic detection methods. We are therefore confident that in the fields of network monitoring and security visual analytics applications will quickly find their way from research into practice by combining human background knowledge and intelligence with the speed and accuracy of computers.