SDN as Active Measurement Infrastructure

Active measurements are integral to the operation and management of networks, and invaluable to supporting empirical network research. Unfortunately, it is often cost-prohibitive and logistically difficult to widely deploy measurement nodes, especially in the core. In this work, we consider the feasibility of tightly integrating measurement within the infrastructure by using Software Defined Networks (SDNs). We introduce "SDN as Active Measurement Infrastructure" (SAAMI) to enable measurements to originate from any location where SDN is deployed, removing the need for dedicated measurement nodes and increasing vantage point diversity. We implement ping and traceroute using SAAMI, as well as a proof-of-concept custom measurement protocol to demonstrate the power and ease of SAAMI's open framework. Via a large-scale measurement campaign using SDN switches as vantage points, we show that SAAMI is accurate, scalable, and extensible

Active Topology Inference using Network Coding

Our goal is to infer the topology of a network when (i) we can send probes between sources and receivers at the edge of the network and (ii) intermediate nodes can perform simple network coding operations, i.e., additions. Our key intuition is that network coding introduces topology-dependent correlation in the observations at the receivers, which can be exploited to infer the topology. For undirected tree topologies, we design hierarchical clustering algorithms, building on our prior work. For directed acyclic graphs (DAGs), first we decompose the topology into a number of two-source, two-receiver (2-by-2) subnetwork components and then we merge these components to reconstruct the topology. Our approach for DAGs builds on prior work on tomography, and improves upon it by employing network coding to accurately distinguish among all different 2-by-2 components. We evaluate our algorithms through simulation of a number of realistic topologies and compare them to active tomographic techniques without network coding. We also make connections between our approach and alternatives, including passive inference, traceroute, and packet marking

Efficient Passive ICS Device Discovery and Identification by MAC Address Correlation

Owing to a growing number of attacks, the assessment of Industrial Control Systems (ICSs) has gained in importance. An integral part of an assessment is the creation of a detailed inventory of all connected devices, enabling vulnerability evaluations. For this purpose, scans of networks are crucial. Active scanning, which generates irregular traffic, is a method to get an overview of connected and active devices. Since such additional traffic may lead to an unexpected behavior of devices, active scanning methods should be avoided in critical infrastructure networks. In such cases, passive network monitoring offers an alternative, which is often used in conjunction with complex deep-packet inspection techniques. There are very few publications on lightweight passive scanning methodologies for industrial networks. In this paper, we propose a lightweight passive network monitoring technique using an efficient Media Access Control (MAC) address-based identification of industrial devices. Based on an incomplete set of known MAC address to device associations, the presented method can guess correct device and vendor information. Proving the feasibility of the method, an implementation is also introduced and evaluated regarding its efficiency. The feasibility of predicting a specific device/vendor combination is demonstrated by having similar devices in the database. In our ICS testbed, we reached a host discovery rate of 100% at an identification rate of more than 66%, outperforming the results of existing tools.Comment: http://dx.doi.org/10.14236/ewic/ICS2018.