Network flows build the basis of modern network data analysis by aggregating properties of network packets with common characteristics. A consistent and unambiguous definition of the network flow concept is an indispensable prerequisite and starting point for reproducible network research. However, in today’s practice, the flow output of distinct flow exporters, which is software to generate flows from observed network packets, varies substantially on identical network packet stream input. In this paper we present an in-depth comparison of different flow exporters and show how their outputs differ significantly. We argue that this substantially impairs reproducibility for traffic analysis research. We first present the detailed flow definition of the ipfix standard including explanations and examples, analyze design and implementation of existing flow exporters, and explore the reasons why many projects and publications chose to implement their own flow exporters. Based on this analysis we highlight the main challenges in the flow exporting process and present a detailed tutorial on how to design and implement a flow exporter such that it yields consistent, reproducible output. Based on the tutorial’s theoretical analysis and lessons learned we present design and main concepts of a versatile, flexible, and open source flow exporting solution called go-flows that generates deterministic, reproducible network flows. Finally, we present a flow-by-flow comparison of the analyzed flow exporters’ output, explore the differences in terms of their generated flows, compare flow exporter performance, and conclude with guidelines on parameters that play a crucial role in improving the reproducibility of exported flows.

The application of clustering involves the interpretation of objects placed in multi-dimensional spaces. The task of clustering itself is inherently submitted to subjectivity, the optimal solution can be extremely costly to discover and sometimes even unreachable or nonexistent. This fact introduces a trade-off between accuracy and computational effort, moreover given that engineering applications usually work well with suboptimal solutions. In such applied scenarios, cluster validation is mandatory to refine algorithms and ensure that solutions are meaningful. Validity indices are commonly intended to benchmark diverse clustering setups, therefore they are coefficients with a relative nature, i.e., useful when compared to one another. In this paper, we propose a validation methodology that enables absolute evaluations of clustering results. Our method performs geometric measurements of the solution space and provides a coherent interpretation of the data structure by using indices based on inter- and intra-cluster distances, density, and multimodality within clusters. Conducted tests and comparisons with well-known indices show that our validation methodology improves the robustness of the clustering application for knowledge discovery. While clustering is often performed as a black box technique, our index is construable and therefore allows for the implementation of systems enriched with self-checking capabilities.

The consolidation of encryption and big data in network communications have made deep packet inspection no longer feasible in large networks. Early attack detection requires feature vectors which are easy to extract, process, and analyze, allowing their generation also from encrypted traffic. So far, experts have selected features based on their intuition, previous research, or acritically assuming standards, but there is no general agreement about the features to use for attack detection in a broad scope. We compared five lightweight feature sets that have been proposed in the scientific literature for the last few years, and evaluated them with supervised machine learning. For our experiments, we use the UNSW-NB15 dataset, recently published as a new benchmark for network security. Results showed three remarkable findings: (1) Analysis based on source behavior instead of classic flow profiles is more effective for attack detection; (2) meta-studies on past research can be used to establish satisfactory benchmarks; and (3) features based on packet length are clearly determinant for capturing malicious activity. Our research showed that vectors currently used for attack detection are oversized, their accuracy and speed can be improved, and are to be adapted for dealing with encrypted traffic.

Smart grids utilize communication technologies that make them vulnerable to cyber attacks. The power grid is a critical infrastructure that constitutes a tempting target for sophisticated and well-equipped attackers. In this paper we simulate three malware types capable of attacking smart grid networks in the ns3 simulation environment. First, an aggressive malware type, named the pandemic malware, follows a topological-scan strategy to find and infect all devices on the network in the shortest time possible, via a brute force approach. Next, the more intelligent endemic malware sacrifices speed for stealthiness and operates with a less conspicuous hit-list and permutation-scan strategy. Finally, a highly stealthy malware type called the contagion malware does not scan the network or initiate any connections but rather appends on legitimate communication flows. We define several metrics to express the infection speed, scanning efficiency, stealthiness, and complexity of malware and use those metrics to compare the three malware types. Our simulations provide details on the scanning and propagation behavior of different malware classes. Furthermore, this work allows the assessment of the detectability of different malware types.