Internet topology discovery aims at analyzing one of the most complex distributed systems currently deployed. Usually, it relies on measurement campaigns using hop-limited probes sent with traceroute. However, this probing tool comes with several limits. In particular, some MPLS clouds might obfuscate collected traces. The resulting Internet maps, their inferred properties, and the graph models are thus incomplete and inaccurate. In this paper, we introduce TNT (Trace the Naughty Tunnels), an extension to Paris traceroute for revealing, or at least detect, all MPLS tunnels along a path. First, along with traceroute and ping probes, TNT looks for hints indicating the presence of hidden tunnels. Those hints are peculiar patterns in the resulting output, e.g., significant TTL shifts or duplicate IP addresses. Second, if those hints trigger alarms, TNT launches additional dedicated probing for possibly revealing hidden tunnels. We use GNS3 to reproduce, verify, and understand the limits and capabilities of TNT in a controlled environment. We also calibrate the thresholds at which alarms are triggered through a dedicated measurement campaign. Finally, we deploy TNT on the Archipelago platform and provide a quantified classification of MPLS configurations. All our results, including the data, the code, and the GNS3 experiments, are fully and publicly available.

For years, Internet topology research has been conducted through active measurement. For instance, Caida builds router level topologies on top of IP level traces obtained with traceroute. The resulting graphs contain a significant amount of nodes with a very large degree, often exceeding the actual number of interfaces of a router. Although this property may result from inaccurate alias resolution, we believe that opaque MPLS clouds made of invisible tunnels are the main cause. Using Layer-2 technologies such as MPLS, routers can be configured to hide internal IP hops from traceroute. Consequently, an entry point of an MPLS network appears as the neighbor of all exit points and the whole Layer-3 network turns into a dense mesh of high degree nodes.

Traffic Engineering (TE) is one of the keys for improving packet forwarding in the Internet. It allows IP network operators to finely tune their forwarding paths according to various customer needs. One of the most popular tool available today for optimizing the use of networking resources is MPLS. On the one hand, operators may use MPLS and label distribution mechanisms such as RSVP-TE in conjunction with BGP to define multiple transit paths (for a given edge pair) verifying different constraints on their network. On the other hand, when operators simply enable LDP for distributing MPLS labels in order to improve the scalability of their network, another kind of path diversity may appear thanks to the ECMP feature of IGP routing.

Middleboxes such as firewalls, NAT, proxies, or Deep Packet Inspection play an increasingly important role in various types of IP networks, including enterprise and cellular networks. Recent studies have shed the light on their impact on real traffic and the complexity of managing them. Network operators and researchers have few tools to understand the impact of those boxes on any path. In this paper, we propose tracebox, an extension to the widely used traceroute tool, that is capable of detecting various types of middlebox interference over almost any path. tracebox sends IP packets containing TCP segments with different TTL values and analyses the packet encapsulated in the returned ICMP messages. Further, as recent routers quote, in the ICMP message, the entire IP packet that they received, tracebox is able to detect any modification performed by upstream middleboxes. In addition, tracebox can often pinpoint the network hop where the middlebox interference occurs. We evaluate tracebox with measurements performed on PlanetLab nodes. Our analysis reveals various types of middleboxes that were not expected on such an experimental testbed supposed to be connected to the Internet without any restriction.

Fingerprinting networking equipment has many potential applications and benefits in network management and security. More generally, it is useful for the understanding of network structures and their behaviors. In this paper, we describe a simple fingerprinting mechanism based on the initial TTL values used by routers to reply to various probing messages. We show that main classes obtained using this simple mechanism are meaningful to distinguish routers platforms. Besides, it comes at a very low additional cost compared to standard active topology discovery measurements. As a proof of concept, we apply our method to gain more insight on the behavior of MPLS routers and to, thus, more accurately quantify their visible/invisible deployment.