Flow monitoring has become a prevalent method for monitoring traffic in high-speed networks. By focusing on the analysis of flows, rather than individual packets, it is often said to be more scalable than traditional packet-based traffic analysis. Flow monitoring embraces the complete chain of packet observation, flow export using protocols such as NetFlow and IPFIX, data collection, and data analysis. In contrast to what is often assumed, all stages of flow monitoring are closely intertwined. Each of these stages therefore has to be thoroughly understood, before being able to perform sound flow measurements. Otherwise, flow data artifacts and data loss can be the consequence, potentially without being observed. This paper is the first of its kind to provide an integrated tutorial on all stages of a flow monitoring setup. As shown throughout this paper, flow monitoring has evolved from the early nineties into a powerful tool, and additional functionality will certainly be added in the future. We show, for example, how the previously opposing approaches of Deep Packet Inspection and flow monitoring have been united into novel monitoring approaches.

In 2012, the Dutch National Research and Education Network, SURFnet, observed a multitude of Distributed Denial of Service (DDoS) attacks against educational institutions. These attacks were effective enough to cause the online exams of hundreds of students to be cancelled. Surprisingly, these attacks were purchased by students from websites, known as Booters. These sites provide DDoS attacks as a paid service (DDoS-as-a-Service) at costs starting from 1 USD. Since this problem was first identified by SURFnet, Booters have been used repeatedly to perform attacks on schools in SURFnet's constituency. Very little is known, however, about the characteristics of Booters, and particularly how their attacks are structure. This is vital information needed to mitigate these attacks. In this paper we analyse the characteristics of 14 distinct Booters based on more than 250 GB of network data from real attacks. Our findings show that Booters pose a real threat that should not be underestimated, especially since our analysis suggests that they can easily increase their firepower based on their current infrastructure.

SSH attacks are a main area of concern for network managers, due to the danger associated with a successful compromise. Detecting these attacks, and possibly compromised victims, is therefore a crucial activity. Most existing network intrusion detection systems designed for this purpose rely on the inspection of individual packets and, hence, do not scale to today's high-speed networks. To overcome this issue, this paper proposes SSHCure, a flow-based intrusion detection system for SSH attacks. It employs an efficient algorithm for the real-time detection of ongoing attacks and allows identification of compromised attack targets. A prototype implementation of the algorithm, including a graphical user interface, is implemented as a plugin for the popular NfSen monitoring tool. Finally, the detection performance of the system is validated with empirical traffic data.

Flows provide an aggregated view of network traffic by grouping streams of packets. The resulting scalability gain usually excuses the coarser data granularity, as long as the flow data reflects the actual network traffic faithfully. However, it is known that the flow export process may introduce artifacts in the exported data. This paper extends the set of known artifacts by explaining which implementation decisions are causing them. In addition, we verify the artifacts' presence in data from a set of widely-used devices. Our results show that the revealed artifacts are widely spread among different devices from various vendors. We believe that these results provide researchers and operators with important insights for developing robust analysis applications. 1

Flow-based approaches for SSH intrusion detection have been developed to overcome the scalability issues of host-based alternatives. Although the detection of many SSH attacks in a flow-based fashion is fairly straightforward, no insight is typically provided in whether an attack was successful. We address this shortcoming by presenting a detection algorithm for the flow-based detection of compromises, i.e., hosts that have been compromised during an attack. Our algorithm has been implemented as part of our open-source IDS SSHCure and validated using almost 100 servers, workstations and honeypots, featuring an accuracy close to 100%.