Multilevel security (MLS) systems control access to data by formalizing permissible and impermissible information flows between data sources and destinations (e.g., database servers and clients) fixed with distinct security labels. In computer networks, MLS systems have been used to prevent unauthorized data disclosure in shared-infrastructure settings where network hosts and devices may fall within different trust domains (e.g., in multi-tenant cloud networks or wireless mesh networks). However, current MLS systems assume static network behavior—thus preventing the network from being practically usable in the presence of dynamic network events that frequent unstable network environments, including sudden changes in traffic patterns, link failures, and topology changes as a result of device movement or intermittent device connectivity. In this paper, we introduceMLS-Enforcer, a software-defined networking (SDN) controller application that can efficiently deploy network-level MLS policies while retaining the ability to securely relabel network nodes under changing topology state and network traffic demands. We model network adaptivity as an integer linear programming problem that reflects a given security policy. We then introduce heuristic relabeling algorithms that achieve near-optimal performance and are more tractable and efficient for larger networks. We validateMLS-Enforceron several network topologies and traffic loads, demonstrating that it can relabel the network to route 90%+ of flows under normal conditions and quickly converge (on the order of seconds for the heuristic algorithms) under changing needs—from small network structure changes to catastrophic failures. This shows that formally secured networks can feasibly be deployed in diverse, changing, and unpredictable environments.

Intrusion detection systems are a commonly deployed defense that examines network traffic, host operations, or both to detect attacks. However, more attacks bypass IDS defenses each year, and with the sophistication of attacks increasing as well, we must examine new perspectives for intrusion detection. Current intrusion detection systems focus on known attacks and/or vulnerabilities, limiting their ability to identify new attacks, and lack the visibility into all system components necessary to confirm attacks accurately, particularly programs. To change the landscape of intrusion detection, we propose that future IDSs track how attacks evolve across system layers by adapting the concept of attack graphs. Attack graphs were proposed to study how multi-stage attacks could be launched by exploiting known vulnerabilities. Instead of constructing attacks reactively, we propose to apply attack graphs proactively to detect sequences of events that fulfill the requirements for vulnerability exploitation. Using this insight, we examine how to generate modular attack graphs automatically that relate adversary accessibility for each component, called its attack surface, to flaws that provide adversaries with permissions that create threats, called attack states, and exploit operations from those threats, called attack actions. We evaluate the proposed approach by applying it to two case studies: (1) attacks on file retrieval, such as TOCTTOU attacks, and (2) attacks propagated among processes, such as attacks on Shell-shock vulnerabilities. In these case studies, we demonstrate how to leverage existing tools to compute attack graphs automatically and assess the effectiveness of these tools for building complete attack graphs. While we identify some research areas, we also find several reasons why attack graphs can provide a valuable foundation for improving future intrusion detection systems.