Attacking and securing Network Time Protocol

Network Time Protocol (NTP) is used to synchronize time between computer systems communicating over unreliable, variable-latency, and untrusted network paths. Time is critical for many applications; in particular it is heavily utilized by cryptographic protocols. Despite its importance, the community still lacks visibility into the robustness of the NTP ecosystem itself, the integrity of the timing information transmitted by NTP, and the impact that any error in NTP might have upon the security of other protocols that rely on timing information. In this thesis, we seek to accomplish the following broad goals: 1. Demonstrate that the current design presents a security risk, by showing that network attackers can exploit NTP and then use it to attack other core Internet protocols that rely on time. 2. Improve NTP to make it more robust, and rigorously analyze the security of the improved protocol. 3. Establish formal and precise security requirements that should be satisfied by a network time-synchronization protocol, and prove that these are sufficient for the security of other protocols that rely on time. We take the following approach to achieve our goals incrementally. 1. We begin by (a) scrutinizing NTP's core protocol (RFC 5905) and (b) statically analyzing code of its reference implementation to identify vulnerabilities in protocol design, ambiguities in specifications, and flaws in reference implementations. We then leverage these observations to show several off- and on-path denial-of-service and time-shifting attacks on NTP clients. We then show cache-flushing and cache-sticking attacks on DNS(SEC) that leverage NTP. We quantify the attack surface using Internet measurements, and suggest simple countermeasures that can improve the security of NTP and DNS(SEC). 2. Next we move beyond identifying attacks and leverage ideas from Universal Composability (UC) security framework to develop a cryptographic model for attacks on NTP's datagram protocol. We use this model to prove the security of a new backwards-compatible protocol that correctly synchronizes time in the face of both off- and on-path network attackers. 3. Next, we propose general security notions for network time-synchronization protocols within the UC framework and formulate ideal functionalities that capture a number of prevalent forms of time measurement within existing systems. We show how they can be realized by real-world protocols (including but not limited to NTP), and how they can be used to assert security of time-reliant applications-specifically, cryptographic certificates with revocation and expiration times. Our security framework allows for a clear and modular treatment of the use of time in security-sensitive systems. Our work makes the core NTP protocol and its implementations more robust and secure, thus improving the security of applications and protocols that rely on time

Online learning on the programmable dataplane

This thesis makes the case for managing computer networks with datadriven methods automated statistical inference and control based on measurement data and runtime observations—and argues for their tight integration with programmable dataplane hardware to make management decisions faster and from more precise data. Optimisation, defence, and measurement of networked infrastructure are each challenging tasks in their own right, which are currently dominated by the use of hand-crafted heuristic methods. These become harder to reason about and deploy as networks scale in rates and number of forwarding elements, but their design requires expert knowledge and care around unexpected protocol interactions. This makes tailored, per-deployment or -workload solutions infeasible to develop. Recent advances in machine learning offer capable function approximation and closed-loop control which suit many of these tasks. New, programmable dataplane hardware enables more agility in the network— runtime reprogrammability, precise traffic measurement, and low latency on-path processing. The synthesis of these two developments allows complex decisions to be made on previously unusable state, and made quicker by offloading inference to the network. To justify this argument, I advance the state of the art in data-driven defence of networks, novel dataplane-friendly online reinforcement learning algorithms, and in-network data reduction to allow classification of switchscale data. Each requires co-design aware of the network, and of the failure modes of systems and carried traffic. To make online learning possible in the dataplane, I use fixed-point arithmetic and modify classical (non-neural) approaches to take advantage of the SmartNIC compute model and make use of rich device local state. I show that data-driven solutions still require great care to correctly design, but with the right domain expertise they can improve on pathological cases in DDoS defence, such as protecting legitimate UDP traffic. In-network aggregation to histograms is shown to enable accurate classification from fine temporal effects, and allows hosts to scale such classification to far larger flow counts and traffic volume. Moving reinforcement learning to the dataplane is shown to offer substantial benefits to stateaction latency and online learning throughput versus host machines; allowing policies to react faster to fine-grained network events. The dataplane environment is key in making reactive online learning feasible—to port further algorithms and learnt functions, I collate and analyse the strengths of current and future hardware designs, as well as individual algorithms