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The increasing development of cryptocurrencies has brought cryptojacking as a new security threat in which attackers steal computing resources for cryptomining. The digitization of the supply chain is a potential major target for cryptojacking due to the large number of different infrastructures involved. These different infrastructures provide information sources that can be useful to detect cryptojacking, but with a wide variety of data formats and encodings. This paper describes the semantic data aggregator (SDA), a normalization and aggregation system based on data modelling and low-latency processing of data streams that facilitates the integration of heterogeneous information sources. As a use case, the paper describes a cryptomining detection system (CDS) based on network traffic flows processed by a machine learning engine. The results show how the SDA is leveraged in this use case to obtain aggregated information that improves the performance of the CDS. [ABSTRACT FROM AUTHOR]

The paper is focuses on the methods that have come into practice, key functions and software instruments for collecting, analysis and visualization of network traffic statistics. The source of information is NetFlow telemetry data collected from network equipment. In addition to being used by network engineers and technicians, including for the purposes of network monitoring, incident handling, identification of network congestion and the main bandwidth utilizers with details on autonomous systems or IP addresses of sources and recipients, protocols, services and applications, NetFlow data is of interest in the context of monitoring and analysis of network interaction between users, service providers and consumers. The paper provides a detailed description of the developed and implemented on the basis of the new generation National Research Computer Network of Russia of the up-to-date and high-performance software solution for working with network telemetry data; specific examples of the capabilities are given in order to advanced analytics and descriptive data visualization in real time, taking into account the special needs of industry telecommunications networks in the field of research and education. [ABSTRACT FROM AUTHOR]

The uses of machine learning (ML) technologies in the detection of network attacks have been proven to be effective when designed and evaluated using data samples originating from the same organisational network. However, it has been very challenging to design an ML-based detection system using heterogeneous network data samples originating from different sources and organisations. This is mainly due to privacy concerns and the lack of a universal format of datasets. In this paper, we propose a collaborative cyber threat intelligence sharing scheme to allow multiple organisations to join forces in the design, training, and evaluation of a robust ML-based network intrusion detection system. The threat intelligence sharing scheme utilises two critical aspects for its application; the availability of network data traffic in a common format to allow for the extraction of meaningful patterns across data sources and the adoption of a federated learning mechanism to avoid the necessity of sharing sensitive users’ information between organisations. As a result, each organisation benefits from the intelligence of other organisations while maintaining the privacy of its data internally. In this paper, the framework has been designed and evaluated using two key datasets in a NetFlow format known as NF-UNSW-NB15-v2 and NF-BoT-IoT-v2. In addition, two other common scenarios are considered in the evaluation process; a centralised training method where local data samples are directly shared with other organisations and a localised training method where no threat intelligence is shared. The results demonstrate the efficiency and effectiveness of the proposed framework by designing a universal ML model effectively classifying various benign and intrusive traffic types originating from multiple organisations without the need for inter-organisational data exchange. [ABSTRACT FROM AUTHOR]

With the massive resources and strategies accessible to attackers, countering Denial of Service (DoS) attacks is getting increasingly difficult. One of these techniques is application-layer DoS. Due to these challenges, network security has become increasingly more challenging to ensure. Hypertext Transfer Protocol (HTTP), Domain Name Service (DNS), Simple Mail Transfer Protocol (SMTP), and other application protocols have had increased attacks over the past several years. It is common for application-layer attacks to concentrate on these protocols because attackers can exploit some weaknesses. Flood and "low and slow" attacks are examples of application-layer attacks. They target weaknesses in HTTP, the most extensively used application-layer protocol on the Internet. Our experiment proposes a generalized detection approach to identify features for application-layer DoS attacks that is not specific to a single slow DoS attack. We combine four application-layer DoS attack datasets: Slow Read, HTTP POST, Slowloris, and Apache Range Header. We perform a feature-scaling technique that applies a normalization filter to the combined dataset. We perform a feature extraction technique, Principal Component Analysis (PCA), on the combined dataset to reduce dimensionality. We examine ways to enhance machine learning techniques for detecting slow application-layer DoS attacks that employ these methodologies. The machine learners effectively identify multiple slow DoS attacks, according to our findings. The experiment shows that classifiers are good predictors when combined with our selected Netflow characteristics and feature selection techniques. [ABSTRACT FROM AUTHOR]

Network Intrusion Detection Systems (NIDSs) are important tools for the protection of computer networks against increasingly frequent and sophisticated cyber attacks. Recently, a lot of research effort has been dedicated to the development of Machine Learning (ML) based NIDSs. As in any ML-based application, the availability of high-quality datasets is critical for the training and evaluation of ML-based NIDS. One of the key problems with the currently available NIDS datasets is the lack of a standard feature set. The use of a unique and proprietary set of features for each of the publicly available datasets makes it virtually impossible to compare the performance of ML-based traffic classifiers on different datasets, and hence to evaluate the ability of these systems to generalise across different network scenarios. To address that limitation, this paper proposes and evaluates standard NIDS feature sets based on the NetFlow network meta-data collection protocol and system. We evaluate and compare two NetFlow-based feature set variants, a version with 12 features, and another one with 43 features. For our evaluation, we converted four widely used NIDS datasets (UNSW-NB15, BoT-IoT, ToN-IoT, CSE-CIC-IDS2018) into new variants with our proposed NetFlow based feature sets. Based on an Extra Tree classifier, we compared the classification performance of the NetFlow-based feature sets with the proprietary feature sets provided with the original datasets. While the smaller feature set cannot match the classification performance of the proprietary feature sets, the larger set with 43 NetFlow features, surprisingly achieves a consistently higher classification performance compared to the original feature set, which was tailored to each of the considered NIDS datasets. The proposed NetFlow-based NIDS feature set, together with four benchmark datasets, made available to the research community, allow a fair comparison of ML-based network traffic classifiers across different NIDS datasets. We believe that having a standard feature set is critical for allowing a more rigorous and thorough evaluation of ML-based NIDSs and that it can help bridge the gap between academic research and the practical deployment of such systems. [ABSTRACT FROM AUTHOR]

Illicit cryptocurrency mining has become one of the prevalent methods for monetization of computer security incidents. In this attack, victims' computing resources are abused to mine cryptocurrency for the benefit of attackers. The most popular illicitly mined digital coin is Monero as it provides strong anonymity and is efficiently mined on CPUs.Illicit mining crucially relies on communication between compromised systems and remote mining pools using the de facto standard protocol Stratum. While prior research primarily focused on endpoint-based detection of in-browser mining, in this paper, we address network-based detection of cryptomining malware in general. We propose XMR-Ray, a machine learning detector using novel features based on reconstructing the Stratum protocol from raw NetFlow records. Our detector is trained offline using only mining traffic and does not require privacy-sensitive normal network traffic, which facilitates its adoption and integration.In our experiments, XMR-Ray attained 98.94% detection rate at 0.05% false alarm rate, outperforming the closest competitor. Our evaluation furthermore demonstrates that it reliably detects previously unseen mining pools, is robust against common obfuscation techniques such as encryption and proxies, and is applicable to mining in the browser or by compiled binaries. Finally, by deploying our detector in a large university network, we show its effectiveness in protecting real-world systems. [ABSTRACT FROM AUTHOR]

The inherent features of software-defined networking (SDN) architecture revolutionize traditional network infrastructure and provide the opportunity for integrated and centralized network monitoring. One of the shortcomings of SDNs is related to its high vulnerability to distributed denial of service attacks and other similar ones. In this paper, a novel multi-stage modular approach is proposed for detecting and mitigating security anomalies in SDN environment (SADM-SDNC). The proposed approach uses NetFlow protocol for gathering information and generating dataset and information gain ratio in order to select the effective features. Also, the C-support vector classification algorithm with radial basis function kernel, and features of Floodlight controller for developing a structure with desirable performance were used in the proposed scheme. The experimental results demonstrate that the proposed approach performs better than other methods in terms of enhancing accuracy and detection rate, and reducing classification error and false alarm rate, which were measured as 99.67%, 99.26%, 0.33%, and 0.08% respectively. Finally, thanks to utilizing REST API and Static Entry Pusher technologies in the Floodlight controller, it makes it possible to disconnect any communications with the attacking factors and remove destructive users. [ABSTRACT FROM AUTHOR]

The implementation of a set of measures aimed to regular monitoring and analyzing the activity of users of the National Research Computer Network of Russia in the inter-network interaction, evaluation of the level of its involvement in joint research projects, the intensity of using the technological infrastructure of Russian and the world's national research and education networks are discussed in the paper. Information on the developed methods, hardware and software solutions based on NetFlow data and provided collecting, analysis and visualization of network traffic is given; some results of work in identifying and visualizing of data exchange and the main directions of network connectivity are presented and discussed. [ABSTRACT FROM AUTHOR]

With the network expansion, the development of information highway, and the numerous data generated by applications, Netflow log size has been rapidly expanding. This paper proposes the use of visualization techniques to quickly and effectively identify network attacks and abnormal events, as well as perceive network security situation. A 2T (combination with Time-series and Treemap) graph visualization system, named Seeflow, is developed, which uses information entropy of Netflow’s features to draw a Time-series graph and use cross-entropies to distinguish between the normal and abnormal flow stream. Time-series graph can overview the network state from macro level. And Treemap graph is used to drill down into details from micro level. In addition, the exponential function is used to conduct quantitative analysis for the performance of Treemap. The Seeflow system also creates graphical features to visually analyze attacks and find interesting patterns. In experiment, VAST Challenge2013 competition dataset is analyzed by Seeflow system. Comparing with the prize-winning works shows that Seeflow can intuitively display network security situation from both of macro and micro level and effectively identify network attacks as well as support decision-making. [ABSTRACT FROM AUTHOR]

This is the report on the Workshop on Flow-Based Network Management, held within the 37th IRTF NMRG meeting, during IETF 93, on 24th July 2015, in Prague, Czech Republic. Following the tradition of the IRTF NMRG, the workshop focused on technologies, developments, and challenges of using flow-level traffic measurements for network management. [ABSTRACT FROM AUTHOR]

New web technologies led to the development of browser applications for data analysis. Modern browser engines allow for building interactive real-time visualization applications that enable efficient ways to understand complex data. We present Flow-Inspector, a highly interactive open-source web framework for visualizing network flow data using latest web technologies. Flow-Inspector includes a backend for processing and storing large-scale network flow data, as well as a JavaScript-based web application capable to display and manipulate traffic information in real-time. This work provides operators with a toolkit to analyze their networks and enables the scientific community to create new and innovative visualizations of traffic data with an extensible framework. We demonstrate the applicability of our approach by implementing several different visualization components that help to identify topological characteristics in network flows. [ABSTRACT FROM AUTHOR]

Visualizing communication logs, like NetFlow records, is extremely useful for numerous tasks that need to analyze network traffic traces, like network planning, performance monitoring, and troubleshooting. Communication logs, however, can be massive, which necessitates designing effective visualization techniques for large data sets. To address this problem, we introduce a novel network traffic visualization scheme based on the key ideas of (1) exploiting frequent itemset mining (FIM) to visualize a succinct set of interesting traffic patterns extracted from large traces of communication logs; and (2) visualizing extracted patterns as hypergraphs that clearly display multi-attribute associations. We demonstrate case studies that support the utility of our visualization scheme and show that it enables the visualization of substantially larger data sets than existing network traffic visualization schemes based on parallel-coordinate plots or graphs. For example, we show that our scheme can easily visualize the patterns of more than 41 million NetFlow records. Previous research has explored using parallel-coordinate plots for visualizing network traffic flows. However, such plots do not scale to data sets with thousands of even millions of flows. [ABSTRACT FROM AUTHOR]

The Network Management Research Group (NMRG) organized in 2010 the Third Workshop on the Usage of NetFlow/IPFIX in Network Management, as part of the 78th IETF Meeting in Maastricht. Yearly organized since 2007, the workshop is an opportunity for people from both academia and industry to discuss the latest developments of the protocol, possibilities for new applications, and practical experiences. This report summarizes the presentations and the main conclusions of the workshop. [ABSTRACT FROM AUTHOR]

Following the success of the First Workshop on the Usage of NetFlow/IPFIX (Pras et al. in J Netw Syst Manag 17(4), 2009) in 2008, the European EMANICS Network of Excellence organized a second workshop in October 2009, held at Jacobs University Bremen. This report summarizes the workshop and presents its main conclusions. [ABSTRACT FROM AUTHOR]

To exchange experiences with, and to discuss ideas on the usage of NetFlow/IPFIX in network management, the IRTF/NMRG, together with the European EMANICS Network of Excellence, organized a one-day workshop in October 2008. This paper presents a report of that meeting. [ABSTRACT FROM AUTHOR]